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[2012.02966v1] Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland

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# Economics > General Economics

**arXiv:2012.02966v1** (econ) 

[Submitted on 5 Dec 2020]

# Title:Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland

Authors:[Daria Loginova](https://arxiv.org/search/econ?searchtype=author&query=Loginova,+D), [Marco Portmann](https://arxiv.org/search/econ?searchtype=author&query=Portmann,+M), [Martin Huber](https://arxiv.org/search/econ?searchtype=author&query=Huber,+M)

View a PDF of the paper titled Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland, by Daria Loginova and 2 other authors

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> Abstract:Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-in-differences estimation using a novel dataset of weekly producer prices for Switzerland and neighbouring countries. We find that TRQs increase prices of most vegetables by more than 20% above the prices in neighbouring countries during the main harvest time for most vegetables and even more than 50% for some vegetables. The effects are stronger for more perishable vegetables and for conventionally produced ones compared with organic vegetables. However, we do not find clear-cut effects of TRQs on the week-to-week price volatility of vegetables although the overall lower price volatility in Switzerland compared with neighbouring countries might be a result of the TRQ system in place. 

Subjects: |  General Economics (econ.GN); Applications (stat.AP)  
---|---  
Cite as: | [arXiv:2012.02966](https://arxiv.org/abs/2012.02966) [econ.GN]  
  | (or  [arXiv:2012.02966v1](https://arxiv.org/abs/2012.02966v1) [econ.GN] for this version)   
  |  <https://doi.org/10.48550/arXiv.2012.02966> Focus to learn more arXiv-issued DOI via DataCite  
  
## Submission history

From: Martin Huber [[view email](/show-email/c04b5c96/2012.02966)]   
**[v1]** Sat, 5 Dec 2020 07:24:52 UTC (1,010 KB)  


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url: http://arxiv.org/abs/2303.04433v1
title: [2303.04433v1] Balancing DSO interests and PV system economics with alternative tariffs
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# Electrical Engineering and Systems Science > Systems and Control

**arXiv:2303.04433v1** (eess) 

[Submitted on 8 Mar 2023]

# Title:Balancing DSO interests and PV system economics with alternative tariffs

Authors:[Alejandro Pena-Bello](https://arxiv.org/search/eess?searchtype=author&query=Pena-Bello,+A), [Robin Junod](https://arxiv.org/search/eess?searchtype=author&query=Junod,+R), [Christophe Ballif](https://arxiv.org/search/eess?searchtype=author&query=Ballif,+C), [Nicolas Wyrsch](https://arxiv.org/search/eess?searchtype=author&query=Wyrsch,+N)

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> Abstract:Distributed rooftop photovoltaics (PV) is one of the pillars of the energy transition. However, the massive integration of distributed PV systems challenges the existing grid, with high amounts of PV injection possibly leading to over-voltage and reverse power flow, with line and transformer overloading, among other issues. Moreover, the increase in PV self-consumption and consequently the reduction of imported electricity poses a problem in recovering Transmission System Operators (TSOs) and Distribution System Operators (DSOs) grid costs, that until now have been directly linked to the amount of electricity consumed due to the volumetric nature of traditional tariffs. To investigate whether alternative tariffs could mitigate PV impacts at the distribution level without hampering PV development, we assess five electricity tariffs that could help the DSOs to recover the costs of maintaining the distribution grid. Additionally, we evaluate how such tariffs may affect private investment in storage and their impact on three types of low-voltage networks (i.e., urban, semi-urban, and rural). We found that tariffs with a capacity-based component promote further adoption of PV and storage. At the same time, they allow the DSOs to recover the grid cost without incurring relevant economic differences for the customer. However, all assessed tariffs were found to have a limited role in mitigating PV impacts at the distribution level. 

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**[v1]** Wed, 8 Mar 2023 08:23:29 UTC (5,743 KB)  


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url: https://pmc.ncbi.nlm.nih.gov/articles/PMC7255316/
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            Are tariffs bad for growth? Yes, say five decades of data from 150 countries - PMC
        

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# Are tariffs bad for growth? Yes, say five decades of data from 150 countries☆

[Davide Furceri](https://pubmed.ncbi.nlm.nih.gov/?term=%22Furceri%20D%22%5BAuthor%5D)

### Davide Furceri

aIMF Research Department and University of Palermo, United States

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### Swarnali A Hannan

bIMF Research Department, United States

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### Jonathan D Ostry

cIMF Research Department and CEPR, United States

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### Andrew K Rose

dNUS-Business, Berkeley-Haas, ABFER, CEPR and NBER, Singapore

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d

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aIMF Research Department and University of Palermo, United States

bIMF Research Department, United States

cIMF Research Department and CEPR, United States

dNUS-Business, Berkeley-Haas, ABFER, CEPR and NBER, Singapore

⁎

Corresponding author at: International Monetary Fund, Research Department, Washington, D.C. 20431, United States. jostry@imf.org

Received 2020 Feb 3; Revised 2020 Feb 17; Accepted 2020 Mar 2; Issue date 2020 July-August.

© 2020 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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PMCID: PMC7255316 PMID: [32834234](https://pubmed.ncbi.nlm.nih.gov/32834234/)

## Abstract

The empirical evidence on the growth effects of import tariffs is sparse in the literature, notwithstanding strong views held by the public and politicians. Using an annual panel of macroeconomic data for 151 countries over 1963–2014, we find that tariff increases are associated with an economically and statistically sizeable and persistent decline in output growth. Thus, fears that the ongoing trade war may be costly for the world economy in terms of foregone output growth are justified.

**Keywords:** Output, VAR, Protectionism, Macroeconomic

## 1\. Introduction

The economics profession is strongly in favor of free trade, but economists have not always done a great job convincing the public and politicians that trade should be free. The economists’ case for free trade is primarily based on: (a) theoretical models of comparative advantage; and (b) empirical evidence, mostly microeconomic, that suggest losses in economic efficiency and welfare from protectionism. But popular debates often focus on headline aggregate figures such as GDP, and what is largely missing from the literature is empirical macro analysis. Accordingly, in this paper we ask whether aggregate data are informative on the costs, if any, of raising tariffs.

The little literature we found does not fill us with confidence that the macroeconomic data will strengthen the professional economist's case. Despite the renewed interest on tariffs, recent studies have mostly concentrated on micro analyses for a handful of countries. The aggregate evidence by and large seems dated, and not particularly compelling; many of the empirical papers tended to find small macroeconomic effects. We were not deterred however, since most studies seem narrow. Bringing to bear more data, covering longer time periods and more countries, might allow us to obtain more precise estimates, and make better inferences about the size of macroeconomic effects. Thus we embarked on a data collection exercise, covering over 150 countries, and more than a half-century of data to tackle our basic question: does an increase in import tariffs boost the size and growth of the aggregate pie (GDP) or shrink it, and if so by how much?

Our efforts in this paper are thus focused on going back to basics—collecting data on tariffs across a wide country and time coverage, and estimating the impact on output using standard empirical-macro methodologies. Our objective is to paint a broad macroeconomic picture, based on traditional macro approaches and covering a large number of countries over a broad span of time, something that is missing in this discussion. As it turns out, our results suggest that using an extended database with substantial country and time coverage, does indeed deliver the goods.

Using aggregated annual data for 151 countries (34 advanced and 117 developing) over 1963–2014, we find that tariffs have economically- and statistically-significant adverse effects on output growth. The impact is persistent and increases with the magnitude of the tariff change. Our baseline econometric model suggests that a one standard deviation increase in the tariff rate (corresponding to a 3.6 percentage points) leads to about a 0.4% decline of output five years later. The estimated decline in output seems related to reduced efficiency in the use of labor across sectors, an appreciation of the real exchange rate which hampers competitiveness (and undercuts possible improvements in the trade balance), higher imported input costs which raise production costs, and intertemporal effects as anticipated tariffs bring forward consumption and output, only to see these macro variables collapse once the tariff is actually imposed. These different channels are fleshed out in a companion paper (Furceri et al., 2019); here, we focus mainly on the headline response of GDP growth.

Overall, our results provide a consistent first set of evidence on the macroeconomic costs from raising import tariffs. Moreover, the costs we identify are likely to be a lower bound on the costs of protectionist policies more generally, as the costs of nontariff barriers are likely higher than those of price-based restrictions.

## 2\. Literature on output-effect of tariffs

> “Major growth effects from trade policy, if they exist, must come from unconventional channels. Conventional trade theory DOES NOT justify claims of huge positive payoffs from free trade.”

> Krugman (2013; slide 10)

Early debates about import protection can be traced back to the period when Britain imposed tariffs in response to the collapse of the gold standard during the Great Depression, with the idea that British unemployment could be reduced by imposing import controls (Cripps & Godley, 1978). Some studies supported this notion by showing that protectionist countries grew faster in the 19th century (Bairoch, 1972, O’Rourke, 2000). Eichengreen (1981), by introducing a process of adjustment and distinguishing between short-run versus long-run effects, showed that tariffs increase output and employment in the short run but could lead to a decline in production in the long run.

A host of other studies find either no or limited negative effects from tariffs (Krugman, 1982; Reitz and Slopek, 2005). Ostry and Rose (1992) show that there is no theoretical presumption about the effects of tariffs on output, with the impact depending on the timing and the expected duration of the tariff shock, the behavior of real wages and exchange rates, the values of the elasticities, and institutional factors (e.g. the exchange rate regime, degree of capital mobility). Consistent with their theoretical review, the authors find no significant effect of tariff changes on the real exchange rate, the real trade balance and real output (foreign or domestic) in their empirical work on five data sets and a non-structural VAR methodology.

The subsequent debates in the literature of the 1990s and 2000s focused on the growth impact of trade and trade policies, with the latter now comprising a host of trade restriction measures apart from tariffs. The connection between trade and growth was found to be broadly positive in these studies (Billmeier and Nannicini, 2013, Dollar, 1992, Feyrer, 2009, Sachs and Warner, 1995), though there has also been considerable debate on the results and the measurement of trade openness (Rodriguez and Rodrik, 2001, Temple, 2000). At the same time, the literature progressed to understand the impact of trade policies, using microeconomic data comprising different industries (see Amiti and Konings, 2007, Grossman and Rogoff, 1995, Topalova and Khandelwal, 2011).

With the renewed interest in tariffs nowadays, there has been a plethora of studies, but most of these have used a narrow range of disaggregated sectoral data and concentrated on a handful of countries, particularly the United States (Amiti, Redding, & Weinstein, 2019; Fajgelbaum, Goldberg, Kennedy, & Khandelwal, 2019). While these studies point toward welfare losses, the generality of the results is limited, given the sectoral focus and the limited coverage of countries. The question thus remains: what is the broad-based macro-level evidence related to the output-effect of tariffs?

## 3\. First things first: data on tariffs

As simple as it sounds, given the straightforward measurability of tariffs compared to other trade barriers, there is no single database that includes tariffs across a broad set of countries for a large time period. Our first step was thus to collate reliable and harmonized (to the extent possible) tariff data using multiple sources. Our tariff data is based on product level data aggregated to the country level, with weights given by the import share of each product, all measured as a fraction of value. The main source is the reform dataset compiled by the Research Department of the IMF (Ostry, Prati, & Spilimbergo, 2009; Prati, Onorato, & Papageorgiou, 2013; Giuliano, Mishra, & Spilimbergo, 2013) which covers an unbalanced sample of 151 countries from 1964 to 2004. We extend the data to 2014 using tariff data from the World Integrated Trade Solution (WITS) and World Development Indicators (WDI). Another valuable feature of this effort is that we do extensive data checks, to ensure that large tariff changes are not spurious, by cross-checking whether each jump is supported by country and policy reports.

Table 1 reports the descriptive statistics, while Fig. 1 plots the evolution of tariffs across income groups. Advanced economies comprise 28 percent of the sample, emerging markets around 44 percent and low-income economies around 28 percent. As expected, there is more variation for both tariff levels and changes across low income countries and emerging markets, compared to advanced economies. Though the mean of our tariff series is negative and the time series plots in Fig. 1 show the general trend of tariffs declining over time, there is considerable variation; 40% of the sample consists of tariff rises (with mean of 1.7 ppt and standard deviation of 3.3), while 53% of observations consist of tariff falls (with mean of −1.8 ppt and standard deviation of 3.4). Appendix A reports the data sources and the list of countries used in our analysis.

### Table 1.

Tariffs, descriptive statistics.

| Number of observations | Mean | Standard deviation | Minimum | Maximum  
---|---|---|---|---|---  
_Tariffs_  
All | 5042 | 11.2 | 13.8 | 0.0 | 161.6  
AM | 1427 | 5.7 | 7.5 | 0.0 | 99.2  
EM | 2161 | 12.5 | 11.6 | 0.0 | 91.4  
LIC | 1422 | 15.1 | 19.2 | 0.0 | 161.6  
_Annual tariff changes_  
All | 4702 | −0.3 | 3.6 | −52.0 | 41.2  
AM | 1392 | −0.3 | 2.2 | −43.6 | 16.1  
EM | 1960 | −0.4 | 3.5 | −24.3 | 27.6  
LIC | 1320 | −0.2 | 4.9 | −52.0 | 41.2  
  
[Open in a new tab](table/tbl0005/)

AM = advanced economies, EM = emerging economies, LIC = low income developing countries.

### Fig. 1.

[![Fig. 1](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b4f/7255316/67d0da17b058/gr1_lrg.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=7255316_gr1_lrg.jpg)

[Open in a new tab](figure/fig0005/)

Tariffs and tariff changes across income groups.

The income groups are equally weighted averages of the tariffs of countries belonging to the particular income group.

## 4\. The effect of tariffs on growth

We use two approaches to evaluate the effect of tariffs on output (growth): (i) some simple stylized-fact charts that show the evolution of growth following tariff hikes; (ii) more formal econometrics using a VAR methodology, so that we can trace the GDP (growth) response of tariffs over time (we explore alternative approaches in our 2019 paper). We then explore some of the possible underlying channels at work. The message from the findings in this section is loud and clear: tariffs have a sizeable negative effect on output growth, with detrimental effects persisting over years.

### 4.1. What do the data say?

Given the extensive country and time coverage of the tariff data at our disposal, there is some value in just letting the data speak. Accordingly, Fig. 2 plots the evolution of output growth following substantial tariff increases. Following closely the approach of Ostry, Berg, and Kothari (2018), we compute residualized growth by taking residuals from regressions of annual real output growth on country- and time-fixed effects. We then take the average of the residualized growth measure across all countries with substantial tariff increases (greater than one/three standard deviation tariff increase(s), where one standard deviation is equivalent to a 3.6 percentage points tariff change).

#### Fig. 2.

[![Fig. 2](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b4f/7255316/3db39bb420df/gr2_lrg.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=7255316_gr2_lrg.jpg)

[Open in a new tab](figure/fig0010/)

Output growth (annual) after tariff hike (percent).

The findings suggest that tariffs have a detrimental effect on output, with the negative effect larger for higher tariff increases and persisting over time, at least over the next four years or so. The residualized growth tends to be in negative territory in all four years following an increase in protectionism. For example, after the second year, the residualized output growth is −0.4/−0.8 for one/three standard deviation(s) increases in tariffs, respectively. After four years, tariff increases are associated with an annual negative output growth of 1.5 percent when tariff increase is above three standard deviations.

### 4.2. Results from a VAR model

We now proceed to more formally estimate the output-effect of tariff changes using VAR (vector autoregressive) analysis. We look at tariff changes that are orthogonal to contemporaneous changes in economic activity, by employing a VAR analysis using a Cholesky decomposition with the following order to recover orthogonal shocks: the change in the log output (i.e., the growth rate), the change in tariff, the change in the log of the real effective exchange rate and the change in trade balance (in percent of GDP).

Fig. 3 shows the estimated dynamic response of output to a one-standard deviation rise in the tariff rate (around 3.6 percentage points). The results suggest that a one standard deviation tariff increase leads to a decline in output growth within the first year. While the effect is not initially statistically significant, output continues to decline in the next few years, and this effect becomes both economically and statistically significant within four years. Ultimately, a one standard deviation tariff increase leads to about a 0.4% decline in output five years later. In an effort to be conservative, we end our analysis at the five-year horizon: the longer-term output-effect of tariffs are actually higher than the estimated medium-term effects.

#### Fig. 3.

[![Fig. 3](https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b4f/7255316/ae5f3562db18/gr3_lrg.jpg)](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=7255316_gr3_lrg.jpg)

[Open in a new tab](figure/fig0015/)

The effect of tariffs on output (%).

_Note_ : The solid bar indicates the response of output to a one standard deviation increase in tariff; the asterisks denote that the results are within 90% confidence band.

These results are consistent with the stylized facts presented earlier: the output growth effect of tariff protectionism is non-negligible, persistent, and increases with the magnitude of the imposed tariffs. The results are robust to alternative orderings within the VAR model. In addition, they are robust to using local projections and instrumental variables approaches, as we show in Furceri et al. (2019). Specifically, using local projections, the results survive a host of robustness checks involving change of the key regressors, variations of the main sample, and inclusion of additional control variables (e.g. a crisis dummy, the nature of the political regime, M2 growth, and contemporaneous real exchange rate shocks).

### 4.3. The channels at work

Why does output fall after tariffs? Furceri et al. (2019) explore some plausible explanations. The wasteful effects of protectionism eventually lead to a substantial reduction in the efficiency with which labor is used, leading to a decline of about 0.9% of labor productivity after five years. Tariffs also lead to a small and marginally-significant increase in unemployment. Both of these effects plausibly lead to a reduction in output. In addition, using industry-level data, Furceri et al. (2019) show that the negative effect of tariffs seems to arise from an increase in the cost of (imported) inputs owing to tariffs. In our companion paper (Furceri et al., 2019), we also find that higher tariffs lead to an appreciation of the real exchange rate and the net effect on the trade balance is small and insignificant.

## 5\. Conclusion: the trade war and output growth

Our results show that tariffs have persistent adverse effects on the size of the pie (GDP). How do our results map the macroeconomic effects of the current trade war? On one side, global trade, industrial production, and growth have either dropped or slowed, due to global trade tensions, tariffs, and trade policy uncertainty (IMF, 2019a, IMF, 2019b). In particular, global growth has slowed from around 3.8 percent in 2017 to 2.9 percent in 2019. On the other hand, economists—those who believe that tariffs have small macroeconomic effects—point out that the tariff effects have not been catastrophic. The trade war has not resulted in a 2008-type global crisis. And, according to IMF pre-COVID-19 projections, global growth was expected to gradually pick up in 2020 and 2021. Where is the persistent output-effect?

Policy responses, particularly accommodative monetary policy stance in key advanced and emerging markets, go a long way to solving this puzzle. The IMF estimates that global growth in 2019 and 2020 (their pre-COVID-19 projections) would have been 0.5 percentage point lower in each year without monetary stimulus. Without such stimulus, therefore, it seems likely that a global recession would have been in the realm of possibility in 2019. The risk of escalated trade tensions going forward, notwithstanding the China-US Phase 1 agreement, in an environment where macro policy space is more limited than before, also does not portend small global macroeconomic effects from tariffs going forward. This round of trade tensions has also brought to the fore the damaging effects of trade policy _uncertainty_ on business confidence and investment decisions. There are also related to supply chain disruptions, which will cumulate over time and might intensify if trade peace is delayed. All this highlights the risk of more damaging first and second round effects going forward. These risks highlight the likelihood of large macroeconomic effects going forward, and should dispel the notion that tariff increases are costless.

## Footnotes

☆

This paper is part of a research project on macroeconomic policy in low-income countries supported by U.K.’s Department for International Development, and is related to earlier research by us, Furceri, Hannan, Ostry, and Rose (2019). The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

## Contributor Information

Davide Furceri, Email: dfurceri@imf.org.

Swarnali A. Hannan, Email: sahmed@imf.org.

Jonathan D. Ostry, Email: jostry@imf.org.

Andrew K. Rose, Email: bizaro@nus.edu.sg.

## Appendix A.

Table A1, Table A2 provide the data sources and the list of the countries included in the analysis.

### Table A1.

Data sources for country-level analysis.

Indicator | Source  
---|---  
Total employment (persons, millions) | World Economic Outlook (WEO)  
Unemployment rate (percent) | WEO and World Development Indicators from World Bank (WDI)  
Gross Domestic Product in constant prices (national currency, billions) | WEO and WDI  
Growth of Real GDP Exp. In Current Oct. Pub. (%) | WEO  
Real effective exchange rate (2010=100) | Information Notice System (IMF)  
Gini net mean of 100 | The Standardized World Income Inequality Database (SWIID)  
Tariff rates | Compiled by IMF Research Department (Prati, Onorato, and Papageorgiou 2013; Giuliano, Mishra, and Spilimbergo 2013). Underlying sources are the WITS, WDI, WTO, GATT, BTN (Brussels Customs Union database)  
Trade balance as a share of GDP; Trade balance is computed using exports of goods and services, and imports of goods and services. Exports, imports and GDP are in constant prices (national currency, billions) | WEO and WDI  
Crises | Leaven and Valencia (2010)  
Wars, political regime | Polity database  
Instruments for tariff | Author calculation using data from WDI and IMF Direction of Trade Statistics  
  
[Open in a new tab](table/tbl0010/)

### Table A2.

List of countries in country-level analysis.

Albania | China | Hungary | Moldova | Singapore  
---|---|---|---|---  
Algeria | Colombia | Iceland | Mongolia | Slovak Republic  
Angola | Comoros | India | Montenegro, Rep. of | Slovenia  
Antigua and Barbuda | Congo, Republic of | Indonesia | Morocco | South Africa  
Argentina | Costa Rica | Iran | Mozambique | Spain  
Armenia | Croatia | Ireland | Myanmar | Sri Lanka  
Australia | Cyprus | Israel | Namibia | St. Lucia  
Austria | Czech Republic | Italy | Nepal | Swaziland  
Azerbaijan | Cote d’Ivoire | Jamaica | Netherlands | Sweden  
Bahrain | Denmark | Japan | New Zealand | Taiwan Province of China  
Bangladesh | Dominica | Jordan | Nicaragua | Tanzania  
Barbados | Dominican Republic | Kazakhstan | Niger | Thailand  
Belarus | Ecuador | Kenya | Nigeria | Togo  
Belgium | Egypt | Korea | Norway | Tonga  
Belize | El Salvador | Kuwait | Oman | Trinidad and Tobago  
Benin | Estonia | Kyrgyz Republic | Pakistan | Tunisia  
Bolivia | Ethiopia | Lao P.D.R. | Panama | Turkey  
Bosnia and Herzegovina | Finland | Latvia | Papua New Guinea | Turkmenistan  
Botswana | France | Lebanon | Paraguay | Uganda  
Brazil | Gabon | Lithuania | Peru | Ukraine  
Brunei Darussalam | Gambia, The | Luxembourg | Philippines | United Arab Emirates  
Bulgaria | Germany | Macedonia, FYR | Poland | United Kingdom  
Burkina Faso | Ghana | Madagascar | Portugal | United States  
Burundi | Greece | Malawi | Qatar | Uruguay  
Cabo Verde | Guatemala | Malaysia | Romania | Uzbekistan  
Cambodia | Guinea | Mali | Russia | Vanuatu  
Cameroon | Guinea-Bissau | Malta | Rwanda | Venezuela  
Canada | Haiti | Mauritania | Saudi Arabia | Vietnam  
Central African Republic | Honduras | Mauritius | Senegal | Yemen  
Chad | Hong Kong SAR | Mexico | Sierra Leone | Zambia  
Chile |  |  |  |   
  
[Open in a new tab](table/tbl0015/)

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url: http://arxiv.org/abs/1706.09365v2
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[1706.09365v2] Bilateral multifactor CES general equilibrium with state-replicating Armington elasticities

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# Quantitative Finance > Economics

**arXiv:1706.09365v2** (q-fin) 

[Submitted on 28 Jun 2017 ([v1](https://arxiv.org/abs/1706.09365v1)), last revised 20 Jul 2017 (this version, v2)]

# Title:Bilateral multifactor CES general equilibrium with state-replicating Armington elasticities

Authors:[Jiyoung Kim](https://arxiv.org/search/q-fin?searchtype=author&query=Kim,+J), [Satoshi Nakano](https://arxiv.org/search/q-fin?searchtype=author&query=Nakano,+S), [Kazuhiko Nishimura](https://arxiv.org/search/q-fin?searchtype=author&query=Nishimura,+K)

View a PDF of the paper titled Bilateral multifactor CES general equilibrium with state-replicating Armington elasticities, by Jiyoung Kim and 2 other authors

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> Abstract:We measure elasticity of substitution between foreign and domestic commodities by two-point calibration such that the Armington aggregator can replicate the two temporally distant observations of market shares and prices. Along with the sectoral multifactor CES elasticities which we estimate by regression using a set of disaggregated linked input--output observations, we integrate domestic production of two countries, namely, Japan and the Republic of Korea, with bilateral trade models and construct a bilateral general equilibrium model. Finally, we make an assessment of a tariff elimination scheme between the two countries. 

Subjects: |  General Economics (econ.GN)  
---|---  
Cite as: | [arXiv:1706.09365](https://arxiv.org/abs/1706.09365) [q-fin.EC]  
  | (or  [arXiv:1706.09365v2](https://arxiv.org/abs/1706.09365v2) [q-fin.EC] for this version)   
  |  <https://doi.org/10.48550/arXiv.1706.09365> Focus to learn more arXiv-issued DOI via DataCite  
Journal reference: | Asia-Pacific Journal of Regional Science 2018  
Related DOI:  | <https://doi.org/10.1007/s41685-017-0068-7> Focus to learn more DOI(s) linking to related resources   
  
## Submission history

From: Kazuhiko Nishimura [[view email](/show-email/66d34577/1706.09365)]   
**[[v1]](/abs/1706.09365v1)** Wed, 28 Jun 2017 17:06:38 UTC (174 KB)  
**[v2]** Thu, 20 Jul 2017 14:52:34 UTC (175 KB)  


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# metadata

key: description
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Abstract page for arXiv paper 1710.01359v2: Multi-Period Coordinated Management of Electric Vehicles in Zonal Power Markets: A Convex Relaxation Approach


key: og_title
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Multi-Period Coordinated Management of Electric Vehicles in Zonal Power Markets: A Convex Relaxation Approach


key: og_description
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In recent years, developments in plug in hybrid electric vehicles have provided various environmental and economic advantages. In the future smart grids, electric vehicles are seen as an important means of transportation to reduce greenhouse gas emissions. One of the main issues regarding to this sort of vehicles is managing their charging time to prevent high peak loads over time. Deploying advanced metering and automatic chargers can be a practical way not only for the vehicle owners to manage their energy consumption, but also for the utilities to manage the electricity load during the day by shifting the charging loads to the off-peak periods. Additionally, an efficient charging schedule can reduce the user's electricity bill cost. In this paper we propose a new coordinated multi-period management model for electric vehicles charging scheduling based on multi-objective approach, aiming at optimizing customers charging cost. In the proposed method, a stochastic model is given for starting time of charging, which makes the method a practical tool for simulating the vehicle owners charging behavior effectively. In order to verify the effectiveness of the proposed method, two market policies are used as case studies. The computation results can be used to evaluate the impact of electric vehicles scheduling on economic performance of smart grid.


key: description
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While the principle of lower emissions behind electric vehicles is commendable, the environmental impact of battery production is still up for debate.


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https://u4d2z7k9.rocketcdn.me/wp-content/uploads/2023/01/Untitled-1024-×-683px-2023-01-10T152926.620.jpg


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Abstract page for arXiv paper 2104.14287v1: Electric cars, assessment of green nature vis a vis conventional fuel driven cars


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A comprehensive analysis of energy requirements and emissions associated with electric vehicles, ranging from mining and making the rare-earth magnets required in electric motor to assembling the Li-ion battery, including charging and regular running of the electric vehicles has been performed. A simple, analytical procedure is used to determine the embodied energy and emissions. The objective is to assess the potential of electric cars to reduce green house gases emission to limit global warming to < 1.5 degrees C by the Year 2050 as per IPCC recommendations and also to compare them with conventional fuel driven cars. The combined embodied energy for Nd- and Dy-metals production which are required in electric motors and battery assembly for 150 million cars, projected to be on the road in the year 2050 is ~ 1500 TWh and the CO2 emissions is found to be > 600 MT. The emissions includes carbon intensity of electrical energy required to run these electric vehicles. The projected emissions due to fossil fuels, gasoline production as well as burning it in combustion engines however is only 412 MT, far less than that due to electric vehicles. The main contributor to emissions from electric vehicles is the battery assembling process which releases ~ 379 MT of CO2-e gases. The emissions from both electric vehicles as well as combustion engine vehicles scale linearly with the number of vehicles, indicating that a breakeven is not possible with the currently available manufacturing technologies. These results clearly show that significant technological developments have to take place in electric vehicles so that they become environmentally better placed compared to combustion engine based cars.


key: description
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Abstract page for arXiv paper 2410.09330v1: A Framework to Estimate Life Cycle Emissions for Vehicle-Integrated Photovoltaic Systems


key: og_description
value:
This paper presents a framework to estimate the environmental impact of solar electric vehicles, accounting for the emissions caused by photovoltaic system production as well as vehicle use. We leverage a cradle-to-gate life cycle assessment to estimate the greenhouse gas emissions of the vehicle-integrated photovoltaic system, from the raw material extraction to the final panel assembly, including the effect of the electricity mix both at the factory location and in the country of use. %the vehicle's life cycle, considering both Furthermore, we modify an existing optimization framework for battery electric vehicles to optimally design a solar electric vehicle and estimate its energy consumption. We showcase our framework by analyzing a case study where the mono-crystalline silicon extraction and refinement processes occur in China, while the final assembly of the panel is in The Netherlands, generating 118 kg of CO2 equivalents per square meter of solar panel. The results suggest that it is generally beneficial to operate solar electric vehicles in countries with a high irradiation index. However, when the local electricity mix already displays a low carbon intensity, the additional emissions introduced by the panel are unnecessary, requiring a longer vehicle lifetime to reach an advantageous emission balance.


key: description
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Abstract page for arXiv paper 2301.03779v1: Estimating required flexibility for secure distribution grid operation considering the uncertainties of EV and PV


key: og_title
value:
Estimating required flexibility for secure distribution grid operation considering the uncertainties of EV and PV


key: og_description
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Renewable energy productions and electrification of mobility are promising solutions to reduce greenhouse gas emissions. Their effective integration in a power grid encounters several challenges. The uncertain nature of renewable energy productions as well as stochastic consumptions of electric vehicles introduce remarkable intermittency to a distribution grid and results in bi-uncertain characteristics of both supply and demand sides. One way to verify the secure grid operation within acceptable voltage and loading levels is to assess its required flexibility considering possible boundaries of uncertain variables. In this paper, first a comprehensive linear model of distribution grid considering all pertaining constraints is presented. Then, a flexibility estimation technique is proposed based on the feasibility study of the uncertain space of photovoltaic power productions and load containing electric vehicles. The proposed methodology uses grid monitoring data to determine grid state and to model uncertain parameters. The model is applied on a real low voltage (LV) system equipped with grid monitoring devices.


key: description
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Are electric vehicles really better for the environment? EVs produce fewer emissions, but that’s not the whole story. Read more to understand the pros & cons.


key: og_image
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https://cdn.prod.website-files.com/62733f1361e2ad64f8790984/62740196a573d7ed6af7f8af_622a722904b5fd6cad562585_sustain.life-electric-vehicles-environment-thumb.jpeg


key: description
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Abstract page for arXiv paper 1806.06947v1: Forecasting the value of battery electric vehicles compared to internal combustion engine vehicles: the influence of driving range and battery technology


key: og_title
value:
Forecasting the value of battery electric vehicles compared to internal combustion engine vehicles: the influence of driving range and battery technology


key: og_description
value:
Battery electric vehicles (BEVs) are now clearly a promising candidate in addressing the environmental problems associated with conventional internal combustion engine vehicles (ICEVs). However, BEVs, unlike ICEVs, are still not widely accepted in the automobile market but continuing technological change could overcome this barrier. The aim of this study is to assess and forecast whether and when design changes and technological improvements related to major challenges in driving range and battery cost will make the user value of BEVs greater than the user value of ICEVs. Specifically, we estimate the relative user value of BEVs and ICEVs resulting after design modifications to achieve different driving ranges by considering the engineering trade-offs based on a vehicle simulation. Then, we analyze when the relative user value of BEVs is expected to exceed ICEVs as the energy density and cost of batteries improve because of ongoing technological change. Our analysis demonstrates that the relative value of BEVs is lower than that of ICEVs because BEVs have high battery cost and high cost of time spent recharging despite high torque, high fuel efficiency, and low fuel cost. Moreover, we found the relative value differences between BEVs and ICEVs are found to be less in high performance large cars than in low performance compact cars because BEVs can achieve high acceleration performance more easily than ICEVs. In addition, this study predicts that in approximately 2050, high performance large BEVs could have higher relative value than high performance large ICEVs because of technological improvements in batteries; however low performance compact BEVs are still very likely to have significantly lower user value than comparable ICEVs until well beyond 2050.


key: description
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Abstract page for arXiv paper 2410.17282v1: Drivers of Electric Vehicle Adoption in Nigeria: An Extended UTAUT Framework Approach


key: og_description
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Electric vehicles (EVs) represent a significant advancement in automotive technology, utilizing electricity as a power source instead of traditional fossil fuels, while incorporating sophisticated navigation and autopilot systems. These vehicles align with multiple Sustainable Development Goals (SDGs) by offering a more environmentally sustainable alternative to internal combustion engine vehicles (ICEVs). Despite their potential, the adoption of EVs in developing nations such as Nigeria remains constrained. This study expands the Unified Theory of Acceptance and Use of Technology (UTAUT) framework by incorporating key enablers, including poor infrastructure, affordability issues, and government support, within the broader category of facilitating conditions. Additionally, it examines factors such as trust, performance expectations, social influences, and network externalities to identify the primary determinants influencing Nigerian consumers' propensity to adopt EVs. Results show that the percentage increase of H6 (facilitating conditions - behavioral intentions) compared to H5 (network externalities - behavioral intentions) is approximately 32.35%, indicating that traditional drivers significantly influence individuals' willingness to purchase EVs and are particularly strong factors in adoption decisions. The paper concludes with a discussion of these findings and proposes strategies for future research to further explore the barriers and drivers of EV adoption in Nigeria.


key: description
value:
Abstract page for arXiv paper 2312.03137v1: The Role of Net-Zero Energy Residential Buildings in Florida's Energy Transition: Economic Analysis and Technical Benefits


key: og_title
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The Role of Net-Zero Energy Residential Buildings in Florida's Energy Transition: Economic Analysis and Technical Benefits


key: og_description
value:
Net-zero emission policies, coupled with declining costs of renewables, battery storage, and electric vehicles, require both direct and indirect electrification (such as green hydrogen) of the energy infrastructure to address climate change impacts, improve resilience, and lower energy expenses. In this paper, a comprehensive techno-economic analysis was performed that integrated net-zero energy buildings within the context of Florida energy transition by the year 2050. The analysis compares the monthly cost savings of owning photovoltaic (PV) on the roof, battery storage, and Electric Vehicle (EV) to the cost of buying electricity from the grid for both existing and newly built Orlando homes. The impact of income tax credits (ITC) and energy efficiency improvements on monthly savings was taken into account. The levelized cost of electricity for residential PV and PV + battery systems was determined and extended to provide a cost equivalent to gasoline for an average EV. Rooftop solar fueling an EV for 10,000 miles saves \$100 per month over purchasing gasoline. Today, Florida residents can save on their monthly costs of electricity and gasoline if they have both PV on their roof and a battery system (sized for average daily residence load), and benefit from the federal ITC. Florida residents can cost-effectively transition to their own solar, battery storage, and electric vehicle to home systems. Allowing utility-scale solar to be used for on-site EV fast-charging and hydrogen production through electrolysis for fuel cell vehicles or blending with natural gas, minimizing the need for electricity transmission and distribution costs across the power grids.


key: description
value:
Abstract page for arXiv paper 2112.12769v2: Joint Routing of Conventional and Range-Extended Electric Vehicles in a Large Metropolitan Network


key: og_description
value:
Range-extended electric vehicles combine the higher efficiency and environmental benefits of battery-powered electric motors with the longer mileage and autonomy of conventional internal combustion engines. This combination is particularly advantageous for time-constrained delivery routing in dense urban areas, where battery recharging along routes can be too time-consuming to economically justify the use of all-electric vehicles. However, switching from electric to conventional fossil fuel modes also results in higher costs and emissions and lower efficiency. This paper analyzes this heterogeneous vehicle routing problem and describes two solution methods: an exact branch-price-and-cut algorithm and an iterated tabu search metaheuristic. From a methodological perspective, we find that the exact algorithm consistently obtains tight lower bounds that also serve to certify the metaheuristic solutions as near-optimal. From a policy standpoint, we examine a large-scale real-world case study concerning parcel deliveries in the Chicago metropolitan area and quantify various operational metrics including energy costs and vehicle miles traveled. We find that by deploying roughly 20% of range-extended vehicles with a modest all-electric range of 33 miles, parcel distributors can save energy costs by up to 17% while incurring less than 0.5% increase in vehicle miles traveled. Increasing the range to 60 miles further reduces costs by only 4%, which can alternatively be achieved by decreasing the average service time by 1 minute or increasing driver working time by 1 hour. Our study reveals several key areas of improvement on which vehicle manufacturers, distributors, and policy makers can focus their attention.


key: description
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The rapid transition to electric-drive vehicles is taking place globally. Most automakers are adding electric models to their lineups to prepare for the new electric future. From the analysis of the automotive market, it is evident that there is a growing interest in such vehicles. They are expected to account for half the models released after 2030. Electric-drive vehicles include battery-electric vehicles. As indicated in the research literature and emphasized by experts, electric vehicles (EVs) are supposed to be an environmentally friendly alternative to conventional vehicles. The rising number and variety of EVs contribute to a better understanding of their performance. With more EVs on the market, there are problems to be solved and challenges to overcome. This article is the first part of a two-article series reviewing the strengths and weaknesses of EVs. The article analyzes the environmental effects of EVs at each stage of their life cycle, compares large- and small-scale recycling methods, and explores the potential applications of second-life batteries. This article is an attempt to find out how environmentally friendly EVs are.


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Electric Vehicles—An Overview of Current Issues—Part 1—Environmental Impact, Source of Energy, Recycling, and Second Life of Battery


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https://pub.mdpi-res.com/energies/energies-17-00249/article_deploy/html/images/energies-17-00249-g001-550.jpg?1704269271


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Abstract page for arXiv paper 2410.04540v2: Distribution Grids May Be a Barrier To Residential Electrification


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Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, residential electrification can raise electricity demand beyond the safe limits of electrical infrastructure, increasing the risk of blackouts or requiring grid reinforcement that can be slow and expensive. Here, we estimate the physical and economic impacts on distribution grids of electrifying all housing and personal vehicles in each county of the lower 48 United States. We find that space heating is the main driver of grid impacts, with the coldest regions seeing demand peaks up to five times higher than today's peaks. Accommodating electrification of all housing and personal vehicles could require up to 544 GW of distribution grid reinforcement nationally, at a cost of \$320 to \$720 billion, or \$2,600 to \$5,800 per household (95% confidence intervals). However, demand-side management can mitigate demand peaks, reducing grid reinforcement costs by up to 77%.


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Electric vehicles (EVs) are an important part of meeting global goals on climate change. They feature prominently in mitigation pathways that limit warming to well-below 2C or 1.5C, which would be inline with the Paris Agreement’s targets.


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What are the environmental impacts of electric vehicles, and how do they compare to traditional cars?


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Answer: Electric vehicles (EVs) have lower emissions of greenhouse gases and pollutants than traditional cars, but their environmental impact depends on various factors. Some of the main environmental impacts of EVs are:

Greenhouse gas emissions: EVs emit fewer greenhouse gases than traditional ...


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Abstract page for arXiv paper 2210.14908v6: Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?


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Electric vehicle adoption is considered to be a promising pathway for addressing climate change. However, the market for charging stations suffers from a market failure: a lack of EV sales disincentives charging station production, which in turn inhibits mass EV adoption. Charging station subsidies are often discussed as policy levers that can stimulate charging station supply and correct this market failure. Nonetheless, there is limited research examining the extent such subsidies are successful in promoting charging station supply. Using annual data on electric vehicle sales, charging station counts, and subsidy amounts from 57 California counties and a staggered difference-in-differences methodology, I find that charging station subsidies are highly effective: counties that adopt subsidies experience a 36% increase in charging station supply 2 years following subsidy adoption. This finding suggests that governmental intervention can help correct the market failure in the charging station market.


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By the year 2035, the federal government is looking to ban the sales of new gasoline-powered cars in an effort to achieve zero emissions by 2050. To further promote e-mobility, the Biden administration, through the Inflation Reduction Act of 2022, is incentivizing converts by giving tax credits to lower the cost of pur


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By the year 2035, the federal government is looking to ban the sales of new gasoline-powered cars in an effort to achieve zero emissions by 2050. To further promote e-mobility, the Biden administration, through the Inflation Reduction Act of 2022, is incentivizing converts by giving tax credits to lower the cost of purchasing a new or used electric vehicle (EV) – both battery-electric and plug-in hybrid models.
But how "clean" really are EVs?

What are EVs, and how do they work?
Electric vehicles are automobiles powered by electric motors rather than internal combustion engines. Unlike gas-powered cars that run by burning fuel, EVs rely on electricity stored in lithium-ion batteries for energy. When driving, the electric motor uses power from the batteries to propel the vehicle forward. EVs can be charged at home, through public charging stations, or high-speed charging stations.
Learn more on How do Electric cars work in our article.
What are the different kinds of EVs?
There are three main types of EVs:


Battery Electric Vehicles (BEVs): Pure electric vehicles that run solely on electricity stored in large batteries.


Plug-in Hybrid Electric Vehicles (PHEVs): Plug-in hybrids combine an internal combustion engine with a battery, allowing for both electric and gasoline-powered driving.


Hybrid Electric Vehicles (HEVs): Uses a combination of an internal combustion engine and an electric motor but cannot be plugged in for recharging.


How does electric car production affect the environment? 
While electric vehicles are lauded for their eco-friendly operation, the EV production process does have environmental consequences. The extraction and processing of raw materials for electric vehicle batteries, such as lithium and cobalt, can result in habitat disruption and resource depletion. Additionally, manufacturing EV batteries and electric motors requires energy, often sourced from fossil fuels.

However, scientists and car manufacturers have been looking into incorporating recycled raw materials, including valuable metals, in the battery production process. On average, EV batteries can last between 8 to 10 years or 100,000 to 200,000 miles, but some can last longer than the entire life cycle of the EV itself. After the batteries have served their intended purpose, recycling facilities will collect, discharge, dismantle, shred, sort, and refine materials for reuse. The refined materials are used in the production of new batteries or other products, completing the recycling loop.
Benefits of electric cars on the environment
Despite the production-related environmental impacts, electric cars offer several benefits:


Slowing Climate Change: One of the primary advantages of EVs is their contribution to reducing the emission of greenhouse gases. By relying on electricity, which can be generated from renewable sources like solar or wind power, EVs leave a smaller carbon footprint compared to traditional gasoline vehicles.


Reducing Local Air Pollution: Electric cars produce zero tailpipe emissions, improving air quality in urban areas. Traditional vehicles emit pollutants like nitrogen oxides and particulate matter, contributing to respiratory problems and smog. The shift to electric vehicles helps combat these issues.


Less Waste Through Recycling: Electric car battery recycling is a key element in minimizing the environmental impact of EVs. As the industry advances, efficient recycling methods for lithium-ion batteries are being developed, reducing the amount of waste generated from the disposal of used batteries.


Four ways that EVs are better for the environment

Reduced Carbon Emissions
The significant reduction in greenhouse gas emissions from electric vehicles stems from their unique mode of propulsion. Unlike a traditional gasoline car, an electric vehicle produces fewer emissions. The absence of exhaust emissions, particularly carbon dioxide (CO2) and other pollutants associated with burning fossil fuels, is a crucial factor in mitigating climate change. By opting for electric power, EVs directly contribute to lowering the concentration of greenhouse gases in the atmosphere, thereby slowing the pace of global warming and its associated adverse effects on ecosystems and weather patterns.
Energy Efficiency
The inherent efficiency of electric motors is a game-changer in the quest for sustainable transportation. Electric motors boast a higher energy conversion efficiency compared to internal combustion engines. When energy is drawn from the vehicle's power source, be it a battery or a plug-in hybrid system, a more significant proportion of that energy is utilized to propel the vehicle. This increased efficiency translates into less energy waste, reducing the overall demand for electricity and, consequently, the environmental impact associated with energy production. This efficiency is a key element in making EVs a greener alternative to traditional vehicles.
Noise Pollution Reduction
Electric vehicles contribute to a quieter and more serene urban environment due to their near-silent operation. A gas-powered car with an internal combustion engine generates noise through an exhaust system, engine combustion, and mechanical components. In contrast, an EV, propelled by an electric motor, produces minimal noise during operation. This reduction in noise pollution has significant implications for the well-being of urban residents. Quieter streets contribute to improved mental health, reduced stress levels, and an overall enhancement of the urban living experience.
Lower Dependence on Fossil Fuels
As electric vehicles become more prevalent, there is a simultaneous push for a transition in the energy grid from fossil fuels to renewable sources. The electricity used to charge EVs can be sourced from renewable energy, such as solar, wind, or hydropower. This shift is crucial in addressing the environmental impact of traditional vehicles, which rely heavily on fossil fuels and other natural gas sources. By decreasing the dependence on non-renewable resources, EVs play a pivotal role in fostering a more sustainable and eco-friendly energy ecosystem. This transition is not only beneficial for reducing carbon dioxide emissions but also contributes to energy security and resilience against the finite nature of fossil fuel resources.
Electric vs. Petrol Car

To truly know which one is better for the environment, let's have a look at the major differences between electric and petrol cars:


Emissions and Environmental Impact:


Electric Cars: EVs emit zero tailpipe emissions during operation, helping reduce carbon emissions, especially when charged using renewable energy sources. However, EV battery production also creates upstream emissions.


Petrol Cars: Conventional vehicles emit pollutants such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter during combustion, contributing to air pollution and climate change.




Operating Costs:


Electric Cars: EVs generally have lower operating costs per mile/kilometer due to the efficiency of electric motors, fewer moving parts, and lower maintenance requirements. Electricity is often cheaper than petrol.


Petrol Cars: An average new gasoline car may have higher fuel and maintenance costs over the vehicle's lifetime, as petrol prices can vary, and internal combustion engines have more components that can wear out.




Performance:


Electric Cars: EVs provide instant torque, resulting in quick acceleration and a smooth driving experience. Electric motors often offer a more responsive and quiet operation.


Petrol Cars: ICE cars offer a wide range of performance options, from fuel-efficient models to high-performance sports cars. However, internal combustion engines may have a brief lag in power delivery.




Range and Charging:


Electric Cars: EV driving range can vary, but advancements in battery technology are increasing it. EV charging infrastructure is expanding, with options for home charging, public charging stations, and fast charging networks.


Petrol Cars: Gas cars generally have a longer driving range per tank of fuel, and refueling at petrol stations is quick. However, the infrastructure is already established, whereas electric charging infrastructure is still developing.




Infrastructure and Convenience:


Electric Cars : EVs require a charging infrastructure, which is expanding but may not be as ubiquitous as petrol stations. Home charging is convenient for many EV owners.


Petrol Cars: ICE vehicles benefit from a well-established network of petrol stations, offering widespread accessibility for refueling.




Government Incentives:


Electric Cars: In many regions, governments offer incentives such as tax credits, rebates, or reduced registration fees to encourage the adoption of electric vehicles. Additionally, the expanded Inflation Reduction Act grants incentives to anyone who installs qualified charging equipment. The Federal Tax Credit gives individuals 30% off Lectron home charging stations plus installation costs (up to $1,000).


Petrol Cars: Typically do not receive specific incentives related to fuel type, as they are the traditional vehicle type.




Vehicle Variety:


Electric Cars: Offer a growing variety of models, including sedans, SUVs, and even trucks. The market is expanding as technology advances.


Petrol Cars: Have a wide range of models, sizes, and types, providing extensive options for consumers.




Resale Value:


Electric Cars: Historically, EVs have faced concerns about resale value due to rapid advancements in battery technology. However, this trend is changing as batteries become more durable and warranties improve.


Petrol Cars: Generally, resale values are influenced by factors such as brand reputation, model popularity, and overall condition.




Are hybrid cars just as good for the environment?
In the context of hybrid vs electric car, although hybrid vehicles are known for their enhanced fuel efficiency, they fall short in environmental benefits when compared to pure electric vehicles. This is due to hybrids' dependence on internal combustion engines, which makes them less efficient in lowering greenhouse gas emissions and carbon pollution than their fully electric counterparts.
FAQs


Can Electric Vehicles Cut Pollution?

Yes, electric vehicles can cut pollution significantly. By relying on electricity, which can be generated from renewable sources, EVs produce fewer emissions compared to traditional vehicles, reducing both air pollution and greenhouse gas emissions.





What are the disadvantages of electric cars on the environment?

The main disadvantages include the environmental impact of battery production, extraction of raw materials, and energy sourcing for manufacturing. However, ongoing advancements in technology and recycling methods aim to address these concerns.





Are electric cars better for the environment?

Overall, electric cars are better for the environment when considering their operational benefits, such as reduced emissions and energy efficiency. However, it's essential to address the environmental impacts associated with their production and disposal. Even after taking battery manufacture into consideration, electric cars are still the more environmentally friendly option.





Does cold weather affect EV cars?

Cold weather can affect the range of electric vehicles due to the impact on battery performance. Batteries may experience reduced efficiency in low temperatures, leading to a temporary decrease in driving range. However, advancements in battery technology are continuously improving EV performance in various weather conditions.


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Abstract page for arXiv paper 2012.04773v2: Effects of Electric Vehicle Adoption for State-Wide Intercity Trips on Emission Saving and Energy Consumption


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Effects of Electric Vehicle Adoption for State-Wide Intercity Trips on Emission Saving and Energy Consumption


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Electric vehicles (EVs) are considered as sustainable alternatives to conventional vehicles, as they reduce emission and fossil fuel dependency. A recent study has proposed a charging infrastructure planning tool to support intercity trips for the estimated EV market share (6 percent) in Michigan for 2030. The main goal of this study is to estimate the emission reduction associated with this electrification rate and infrastructure investment for light duty vehicles. To this end, a state-of-the-art emission estimation framework is proposed to be applied to the state-wide intercity travels. The main contributions of the proposed framework includes: 1) Incorporating a micro emission estimation model for simulated vehicle trajectories of the intercity network of Michigan, 2) Adjusting the micro emission model results considering impacts of monthly travel demand and temperature variations, and heterogeneity of vehicles based on their make, model, and age. The emission estimation framework is then compared with the traditional VMT analysis method as a benchmark. Finally, five different scenarios are explored for EV adoption to assess potential emission savings from the given electrification rate for each scenario. The results suggest an annual CO2 emission savings of 0.58-0.92 million-ton. The CO2 social cost savings may justify the investment on the network electrification. Note that only 3.7 to 8.6 percent of the total EV energy requirements must be provided via the DC fast charger network proposed by the charging infrastructure planning tool. This requires annual energy consumption of 22.15 to 51.76 BWh for the estimated EV market share in Michigan for 2030.


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Abstract page for arXiv paper 2403.13373v1: Charged Momentum: Electric Vehicle Surge in India's 2023 Landscape


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Electric vehicles (EVs) have emerged as a transformative force in India's transportation sector, offering a sustainable solution to the country's growing energy and environmental challenges. Against the backdrop of rapid urbanization, rising pollution levels, and the need for energy security, EVs have gained traction as a viable alternative to traditional internal combustion engine vehicles. This paper provides a comprehensive analysis of the electric vehicle market in India, focusing particularly on the landscape of 2023. It emphasizes key aspects such as the 2023 scenario of EV adoption, the role of indigenous manufacturers, dominant players shaping the market, and the influence of government policies and initiatives, including the FAME I and II schemes. Furthermore, the paper delves into EV sales data for the fiscal year 2023, offering insights into market trends and consumer preferences. By elucidating the current state of EVs in India, this paper aims to contribute to a deeper understanding of the country's transition towards sustainable mobility and its implications for energy, environment, and economy.


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Electric cars are becoming mainstream, but does that mean they’re better for the environment? According to the experts, yes. Read our article to learn more.


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https://images.prismic.io/greenly/4352e912-ce01-4e59-8be4-4fbe7f533dbd_train+v+car+article+image2.jpg?auto=format,compress


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Abstract page for arXiv paper 2404.01358v1: Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists


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Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonists (GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by 2030. Using a Named Entity Recognition (NER) model, our method successfully detected 21 potential ASEs overlooked upon FDA approval, including irritability and numbness. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed drugs, leveraging cutting-edge AI-driven social media analytics. It can increase the safety of new drugs in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.


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Weight loss and low BMI due to an underlying illness have been associated with increased mortality, reduced functional capacity, and diminished quality of life. There is a need for safe, long- term approaches to maintain body weight in patients with ...


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Recent studies have shown that pharmacologic weight loss with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and combination therapies is approaching magnitudes achieved with surgery. However, as more weight loss is achieved, there is concern for potential adverse effects on muscle quantity, …


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pmid:39401279, doi:10.1161/CIRCULATIONAHA.124.067676, Review, Jennifer Linge, Andreas L Birkenfeld, Ian J Neeland, Adaptation, Physiological / drug effects, Animals, Glucagon-Like Peptide-1 Receptor Agonists*, Humans, Muscle, Skeletal* / drug effects, Muscle, Skeletal* / pathology, Obesity / drug therapy, Sarcopenia / drug therapy, Weight Loss* / drug effects, PubMed Abstract, NIH, NLM, NCBI, National Institutes of Health, National Center for Biotechnology Information, National Library of Medicine, MEDLINE


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Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of cardiovascular mortality and is increasingly prevalent in our population. Glucagon-like ...


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glucagon-like peptide-1 receptor agonists,Atherosclerosis,Cardiovascular outcome trials,Inflammation,Mitochondrial dysfunction


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https://images-provider.frontiersin.org/api/ipx/w=1200&f=png/https://www.frontiersin.org/files/Articles/1396656/fphar-15-1396656-HTML/image_m/fphar-15-1396656-g001.jpg


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Scholar Rock is running a proof-of-concept study to mitigate muscle loss and enhance muscle mass in patients using GLP-1 drugs for weight management.


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Cardiovascular disease (CVD) remains a prominent cause of global mortality, primarily driven by atherosclerosis. Diabetes mellitus, as a modifiable risk factor, significantly contributes to atherogenesis. Monocyte recruitment to the intima is a critical step in atherosclerotic plaque formation, involving chemokines and adhesion molecules such as selectins, ICAM-1, and MCP-1. Glucagon-like peptide 1 receptor agonists (GLP-1RAs) are a promising group of drugs for reducing cardiovascular risk in diabetic patients, prompting investigation into their mechanisms of action. This interventional study enrolled 50 diabetes patients with atherosclerotic plaque, administering GLP-1RA for 180 days. Serum concentrations of MCP-1, ICAM-1, and L-selectin were measured before and after treatment. Anthropometric and biochemical parameters were also assessed. GLP-1RA treatment resulted in significant improvements in anthropometric parameters, glycemic control, blood pressure, and biochemical markers of liver steatosis. Biomarker laboratory analysis revealed higher baseline levels of MCP-1, ICAM-1, and L-selectin in diabetic patients with atherosclerotic plaque compared to healthy controls. Following treatment, MCP-1 and L-selectin levels decreased significantly (p < 0.001), while ICAM-1 levels increased (p < 0.001). GLP-1RA treatment in diabetic patients with atherosclerotic plaque leads to favorable changes in serum molecule levels associated with monocyte recruitment to the endothelium. The observed reduction in MCP-1 and L-selectin suggests a potential mechanism underlying GLP-1RA-mediated cardiovascular risk reduction. Further research is warranted to elucidate the precise mechanisms and clinical implications of these findings in diabetic patients with atherosclerosis.


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https://pub.mdpi-res.com/life/life-14-00690/article_deploy/html/images/life-14-00690-g001-550.jpg?1716881986


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Abstract page for arXiv paper 2304.14053v1: Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI


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Precise thigh muscle volumes are crucial to monitor the motor functionality of patients with diseases that may result in various degrees of thigh muscle loss. T1-weighted MRI is the default surrogate to obtain thigh muscle masks due to its contrast between muscle and fat signals. Deep learning approaches have recently been widely used to obtain these masks through segmentation. However, due to the insufficient amount of precise annotations, thigh muscle masks generated by deep learning approaches tend to misclassify intra-muscular fat (IMF) as muscle impacting the analysis of muscle volumetrics. As IMF is infiltrated inside the muscle, human annotations require expertise and time. Thus, precise muscle masks where IMF is excluded are limited in practice. To alleviate this, we propose a few-shot segmentation framework to generate thigh muscle masks excluding IMF. In our framework, we design a novel pseudo-label correction and evaluation scheme, together with a new noise robust loss for exploiting high certainty areas. The proposed framework only takes $1\%$ of the fine-annotated training dataset, and achieves comparable performance with fully supervised methods according to the experimental results.


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Abstract page for arXiv paper 2407.02902v1: Instrumental Variable methods to target Hypothetical Estimands with longitudinal repeated measures data: Application to the STEP 1 trial


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Instrumental Variable methods to target Hypothetical Estimands with longitudinal repeated measures data: Application to the STEP 1 trial


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The STEP 1 randomized trial evaluated the effect of taking semaglutide vs placebo on body weight over a 68 week duration. As with any study evaluating an intervention delivered over a sustained period, non-adherence was observed. This was addressed in the original trial analysis within the Estimand Framework by viewing non-adherence as an intercurrent event. The primary analysis applied a treatment policy strategy which viewed it as an aspect of the treatment regimen, and thus made no adjustment for its presence. A supplementary analysis used a hypothetical strategy, targeting an estimand that would have been realised had all participants adhered, under the assumption that no post-baseline variables confounded adherence and change in body weight. In this paper we propose an alternative Instrumental Variable method to adjust for non-adherence which does not rely on the same `unconfoundedness' assumption and is less vulnerable to positivity violations (e.g., it can give valid results even under conditions where non-adherence is guaranteed). Unlike many previous Instrumental Variable approaches, it makes full use of the repeatedly measured outcome data, and allows for a time-varying effect of treatment adherence on a participant's weight. We show that it provides a natural vehicle for defining two distinct hypothetical estimands: the treatment effect if all participants would have adhered to semaglutide, and the treatment effect if all participants would have adhered to both semaglutide and placebo. When applied to the STEP 1 study, they both suggest a sustained, slowly decaying weight loss effect of semaglutide treatment.


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Abstract page for arXiv paper 2312.01788v1: Functional Magnetic Resonance Imaging Changes and Increased Muscle Pressure in Fibromyalgia: Insights from Prominent Theories of Pain and Muscle Imaging


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Functional Magnetic Resonance Imaging Changes and Increased Muscle Pressure in Fibromyalgia: Insights from Prominent Theories of Pain and Muscle Imaging


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Fibromyalgia is a complicated and multifaceted disorder marked by widespread chronic pain, fatigue, and muscle tenderness. Current explanations for the pathophysiology of this condition include the Central Sensitization Theory, Cytokine Inflammation Theory, Muscle Hypoxia, Muscle Tender Point Theory, and Small Fiber Neuropathy Theory. The objective of this review article is to examine and explain each of these current theories and to provide a background on our current understanding of fibromyalgia. The medical literature on this disorder, as well as on the roles of functional magnetic resonance imaging (fMRI) and elastography as diagnostic tools, was reviewed from the 1970s to early 2023, primarily using the PubMed database. Five prominent theories of fibromyalgia etiology were examined: 1) Central Sensitization Theory; 2) Cytokine Inflammation Theory; 3) Muscle Hypoxia; 4) Muscle Tender Point Theory; and 5) Small Fiber Neuropathy Theory. Previous fMRI studies of FMS have revealed two key findings. First, patients with FMS show altered activation patterns in brain regions involved in pain processing. Second, the connectivity between brain structures in individuals diagnosed with FMS and healthy controls is different. Both of these findings will be expanded upon in this paper.
  The article also explores the potential for future research in fibromyalgia due to the advancements in fMRI and elastography techniques, such as shear wave ultrasound. Increased understanding of the underlying mechanisms contributing to fibromyalgia symptoms is necessary for improved diagnosis and treatment, and advanced imaging techniques can aid in this process.


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Abstract page for arXiv paper 2310.09171v2: Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds


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Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds


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Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model calibration and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, to calibration of passive muscle forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated using Bhargava et al. 2004 and Umberger 2010 models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and was similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed.


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Abstract page for arXiv paper 1606.03910v1: Myocardial Architecture and Patient Variability in Clinical Patterns of Atrial Fibrillation


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Atrial fibrillation (AF) increases the risk of stroke by a factor of four to five and is the most common abnormal heart rhythm. The progression of AF with age, from short self-terminating episodes to persistence, varies between individuals and is poorly understood. An inability to understand and predict variation in AF progression has resulted in less patient-specific therapy. Likewise, it has been a challenge to relate the microstructural features of heart muscle tissue (myocardial architecture) with the emergent temporal clinical patterns of AF. We use a simple model of activation wavefront propagation on an anisotropic structure, mimicking heart muscle tissue, to show how variation in AF behaviour arises naturally from microstructural differences between individuals. We show that the stochastic nature of progressive transversal uncoupling of muscle strands (e.g., due to fibrosis or gap junctional remodelling), as occurs with age, results in variability in AF episode onset time, frequency, duration, burden and progression between individuals. This is consistent with clinical observations. The uncoupling of muscle strands can cause critical architectural patterns in the myocardium. These critical patterns anchor micro-re-entrant wavefronts and thereby trigger AF. It is the number of local critical patterns of uncoupling as opposed to global uncoupling that determines AF progression. This insight may eventually lead to patient specific therapy when it becomes possible to observe the cellular structure of a patient's heart.


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Abstract page for arXiv paper 2411.00071v2: What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass


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What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass


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Musculoskeletal (MSK) models offer a non-invasive way to understand biomechanical loads on joints and tendons, which are difficult to measure directly. Variations in muscle strength, especially relative differences between muscles, significantly impact model outcomes. Typically, scaled generic MSK models use maximum isometric forces that are not adjusted for different demographics, raising concerns about their accuracy. This review provides an overview on experimentally derived strength parameters, including physiological cross-sectional area (PCSA), muscle mass (Mm), and relative muscle mass (%Mm), which is the relative distribution of muscle mass across the leg. We analysed differences by age and sex, and compared open-source lower limb MSK model parameters with experimental data from 57 studies. Our dataset, with records dating back to 1884, shows that uniformly increasing all maximum isometric forces in MSK models does not capture key muscle ratio differences due to age and sex. Males have a higher proportion of muscle mass in the rectus femoris and semimembranosus muscles, while females have a greater relative muscle mass in the pelvic (gluteus maximus and medius) and ankle muscles (tibialis anterior, tibialis posterior, and extensor digitorum longus). Older adults have a higher relative muscle mass in the gluteus medius, while younger individuals show more in the gastrocnemius. Current MSK models do not accurately represent muscle mass distribution for specific age or sex groups, and none of them accurately reflect female muscle mass distribution. Further research is needed to explore musculotendon age- and sex differences.


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Abstract page for arXiv paper 2411.15504v1: Effects of Muscle Synergy during Overhead Work with a Passive Shoulder Exoskeleton: A Case Study


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Objective: Shoulder exoskeletons can effectively assist with overhead work. However, their impacts on muscle synergy remain unclear. The objective is to systematically investigate the effects of the shoulder exoskeleton on muscle synergies during overhead work.Methods: Eight male participants were recruited to perform a screwing task both with (Intervention) and without (Normal) the exoskeleton. Eight muscles were monitored and muscle synergies were extracted using non-negative matrix factorization and electromyographic topographic maps. Results: The number of synergies extracted was the same (n = 2) in both conditions. Specifically, the first synergies in both conditions were identical, with the highest weight of AD and MD; while the second synergies were different between conditions, with highest weight of PM and MD, respectively. As for the first synergy in the Intervention condition, the activation profile significantly decreased, and the average recruitment level and activation duration were significantly lower (p<0.05). The regression analysis for the muscle synergies across conditions shows the changes of muscle synergies did not influence the sparseness of muscle synergies (p=0.7341). In the topographic maps, the mean value exhibited a significant decrease (p<0.001) and the entropy significantly increased (p<0.01). Conclusion: The exoskeleton does not alter the number of synergies and existing major synergies but may induce new synergies. It can also significantly decrease neural activation and may influence the heterogeneity of the distribution of monitored muscle activations. Significance: This study provides insights into the potential mechanisms of exoskeleton-assisted overhead work and guidance on improving the performance of exoskeletons.


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Abstract page for arXiv paper 2404.14100v1: A Method of Joint Angle Estimation Using Only Relative Changes in Muscle Lengths for Tendon-driven Humanoids with Complex Musculoskeletal Structures


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A Method of Joint Angle Estimation Using Only Relative Changes in Muscle Lengths for Tendon-driven Humanoids with Complex Musculoskeletal Structures


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Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and need to be estimated using the changes in muscle lengths. In previous studies, methods using table-search and extended kalman filter have been developed. These methods express the joint-muscle mapping, which is the nonlinear relationship between joint angles and muscle lengths, by using a data table, polynomials, or a neural network. However, due to computational complexity, these methods cannot consider the effects of polyarticular muscles. In this study, considering the limitation of the computational cost, we reduce unnecessary degrees of freedom, divide joints and muscles into several groups, and formulate a joint angle estimation method that takes into account polyarticular muscles. Also, we extend the estimation method to propose a joint angle estimation method using only the relative changes in muscle lengths. By this extension, which does not use absolute muscle lengths, we do not need to execute a difficult calibration of muscle lengths for tendon-driven musculoskeletal humanoids. Finally, we conduct experiments in simulation and actual environments, and verify the effectiveness of this study.


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Abstract page for arXiv paper 2106.10109v1: A Weak Monotonicity Based Muscle Fatigue Detection Algorithm for a Short-Duration Poor Posture Using sEMG Measurements


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A Weak Monotonicity Based Muscle Fatigue Detection Algorithm for a Short-Duration Poor Posture Using sEMG Measurements


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Muscle fatigue is usually defined as a decrease in the ability to produce force. The surface electromyography (sEMG) signals have been widely used to provide information about muscle activities including detecting muscle fatigue by various data-driven techniques such as machine learning and statistical approaches. However, it is well-known that sEMG signals are weak signals (low amplitude of the signals) with a low signal-to-noise ratio, data-driven techniques cannot work well when the quality of the data is poor. In particular, the existing methods are unable to detect muscle fatigue coming from static poses. This work exploits the concept of weak monotonicity, which has been observed in the process of fatigue, to robustly detect muscle fatigue in the presence of measurement noises and human variations. Such a population trend methodology has shown its potential in muscle fatigue detection as demonstrated by the experiment of a static pose.


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Abstract page for arXiv paper 2409.18266v1: Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework


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Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Surface electromyography (sEMG), on the other hand, records muscle bioelectrical signals as the muscle activation. This paper introduced a deep-learning approach to leverage sEMG signals for muscle thickness deformation prediction, eliminating the need for ultrasound measurement. Using a dual-attention framework combining self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, showing potential for applications in clinical diagnostics, sports science, and rehabilitation.


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Abstract page for arXiv paper 2312.14273v1: Towards Non-contact Muscle Activity Estimation using FMCW Radar


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Surface electromyography (sEMG) is a widely used muscle activity monitoring technique. sEMG measures muscle activity through monopolar and bipolar, multi-electrode electrodes. The surface electrodes are placed on the surface of the skin above the target muscle and the received signal can be used to infer the state of the muscle - active, inactive or fatigued - which serves as vital information during neurological and orthopaedic rehabilitation. Additionally, the sEMG signal can also be used for the control of prostheses. sEMG requires contact with the participant's skin and is thus a potentially uncomfortable method for the measurement of muscle activity. Moreover, the setup procedure has been termed time-consuming by sEMG experts and is listed as one of the main barriers to the clinical employment of the technique. Previous studies have shown that architectural changes, particularly muscle deformation, can provide information about the activity of the muscle, providing an alternative to sEMG. In all these studies, the muscle deformation signal is acquired using ultrasound imaging, an approach known as sonomyography (SMG). Despite its advantages, such as improved spatial resolution, SMG is still a contact based approach. In this paper, we propose a non-contact muscle activity monitoring approach that measures the muscle deformation signal using a Frequency Modulated Continuous Wave (FMCW) mmWave radar which we call radiomyography (RMG). In future, this system will enable muscle activation to be measured in an unconstrained and less cumbersome manner for both the person conducting the test and the individual being tested.


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Abstract page for arXiv paper 2106.00652v3: Comprehensive Validation of Automated Whole Body Skeletal Muscle, Adipose Tissue, and Bone Segmentation from 3D CT images for Body Composition Analysis: Towards Extended Body Composition


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Comprehensive Validation of Automated Whole Body Skeletal Muscle, Adipose Tissue, and Bone Segmentation from 3D CT images for Body Composition Analysis: Towards Extended Body Composition


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The latest advances in computer-assisted precision medicine are making it feasible to move from population-wide models that are useful to discover aggregate patterns that hold for group-based analysis to patient-specific models that can drive patient-specific decisions with regard to treatment choices, and predictions of outcomes of treatment. Body Composition is recognized as an important driver and risk factor for a wide variety of diseases, as well as a predictor of individual patient-specific clinical outcomes to treatment choices or surgical interventions. 3D CT images are routinely acquired in the oncological worklows and deliver accurate rendering of internal anatomy and therefore can be used opportunistically to assess the amount of skeletal muscle and adipose tissue compartments. Powerful tools of artificial intelligence such as deep learning are making it feasible now to segment the entire 3D image and generate accurate measurements of all internal anatomy. These will enable the overcoming of the severe bottleneck that existed previously, namely, the need for manual segmentation, which was prohibitive to scale to the hundreds of 2D axial slices that made up a 3D volumetric image. Automated tools such as presented here will now enable harvesting whole-body measurements from 3D CT or MRI images, leading to a new era of discovery of the drivers of various diseases based on individual tissue, organ volume, shape, and functional status. These measurements were hitherto unavailable thereby limiting the field to a very small and limited subset. These discoveries and the potential to perform individual image segmentation with high speed and accuracy are likely to lead to the incorporation of these 3D measures into individual specific treatment planning models related to nutrition, aging, chemotoxicity, surgery and survival after the onset of a major disease such as cancer.


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Abstract page for arXiv paper 1911.02345v1: The Energy Sensor AMPK: Adaptations to Exercise, Nutritional and Hormonal Signals


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To sustain metabolism, intracellular ATP concentration must be regulated within an appropriate range. This coordination is achieved through the function of the AMP-activated protein kinase (AMPK), a cellular "fuel gauge" that is expressed in essentially all eukaryotic cells as heterotrimeric complexes containing catalytic $α$ subunits and regulatory $β$ and $γ$ subunits. When cellular energy status has been compromised, AMPK is activated by increases in AMP:ATP or ADP:ATP ratios and acts to restore energy homeostasis by stimulating energy production via catabolic pathways while decreasing non-essential energy-consuming pathways. Although the primary function of AMPK is to regulate energy homeostasis at a cell-autonomous level, in multicellular organisms, the AMPK system has evolved to interact with hormones to regulate energy intake and expenditure at the whole body level. Thus, AMPK functions as a signaling hub, coordinating anabolic and catabolic pathways to balance nutrient supply with energy demand at both the cellular and whole-body levels. AMPK is activated by various metabolic stresses such as ischemia or hypoxia or glucose deprivation and has both acute and long-term effects on metabolic pathways and key cellular functions. In addition, AMPK appears to be a major sensor of energy demand in exercising muscle and acts both as a multitask gatekeeper and an energy regulator in skeletal muscle. Acute activation of AMPK has been shown to promote glucose transport and fatty acid oxidation while suppressing glycogen synthase activity and protein synthesis. Chronic activation of AMPK induces a shift in muscle fiber type composition, reduces markers of muscle degeneration and enhances muscle oxidative capacity potentially by stimulating mitochondrial biogenesis. Furthermore, recent evidence demonstrates that AMPK may not only regulate metabolism during exercise but also in the recovery phase. AMPK acts as a molecular transducer between exercise and insulin signaling and is necessary for the ability of prior contraction/exercise to increase muscle insulin sensitivity. Based on these observations, drugs that activate AMPK might be expected to be useful in the treatment of metabolic disorders and insulin resistance in various conditions.


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Abstract page for arXiv paper 2310.06191v1: Investigating the Correlation between Force Output, Strains, and Pressure for Active Skeletal Muscle Contractions


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Investigating the Correlation between Force Output, Strains, and Pressure for Active Skeletal Muscle Contractions


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Experimental observations suggest that the force output of the skeletal muscle tissue can be correlated to the intra-muscular pressure generated by the muscle belly. However, pressure often proves difficult to measure through in-vivo tests. Simulations on the other hand, offer a tool to model muscle contractions and analyze the relationship between muscle force generation and deformations as well as pressure outputs, enabling us to gain insight into correlations among experimentally measurable quantities such as principal and volumetric strains, and the force output. In this work, a correlation study is performed using Pearson's and Spearman's correlation coefficients on the force output of the skeletal muscle, the principal and volumetric strains experienced by the muscle and the pressure developed within the muscle belly as the muscle tissue undergoes isometric contractions due to varying activation profiles. The study reveals strong correlations between force output and the strains at all locations of the belly, irrespective of the type of activation profile used. This observation enables estimation on the contribution of various muscle groups to the total force by the experimentally measurable principal and volumetric strains in the muscle belly. It is also observed that pressure does not correlate well with force output due to stress relaxation near the boundary of muscle belly.


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Abstract page for arXiv paper 2410.07682v1: Patterned Structure Muscle : Arbitrary Shaped Wire-driven Artificial Muscle Utilizing Anisotropic Flexible Structure for Musculoskeletal Robots


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Patterned Structure Muscle : Arbitrary Shaped Wire-driven Artificial Muscle Utilizing Anisotropic Flexible Structure for Musculoskeletal Robots


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Muscles of the human body are composed of tiny actuators made up of myosin and actin filaments. They can exert force in various shapes such as curved or flat, under contact forces and deformations from the environment. On the other hand, muscles in musculoskeletal robots so far have faced challenges in generating force in such shapes and environments. To address this issue, we propose Patterned Structure Muscle (PSM), artificial muscles for musculoskeletal robots. PSM utilizes patterned structures with anisotropic characteristics, wire-driven mechanisms, and is made of flexible material Thermoplastic Polyurethane (TPU) using FDM 3D printing. This method enables the creation of various shapes of muscles, such as simple 1 degree-of-freedom (DOF) muscles, Multi-DOF wide area muscles, joint-covering muscles, and branched muscles. We created an upper arm structure using these muscles to demonstrate wide range of motion, lifting heavy objects, and movements through environmental contact. These experiments show that the proposed PSM is capable of operating in various shapes and environments, and is suitable for the muscles of musculoskeletal robots.


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Abstract page for arXiv paper 1712.00522v1: State Estimation For An Agonistic-Antagonistic Muscle System


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Research on assistive technology, rehabilitation, and prosthesis requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic-antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of estimating the state variables and activation signals of the dual muscle system is considered. In this work, parameter uncertainty and unknown inputs are taken into account for the estimation problem. Three observers are presented: a high gain observer, a sliding mode observer, and an adaptive sliding mode observer. Theoretical analysis shows the convergence of the three observers. To facilitate numerical simulations, a backstepping controller is employed to drive the muscle system to track a desired trajectory. Numerical simulations reveal that the three observers are comparable and provide reliable estimates in noise free and noisy cases. The proposed schemes may serve as frameworks for estimation of complex multi-muscle systems, which could lead to intelligent exercise machines for adaptive training and rehabilitation, and adaptive prosthetics and exoskeletons.


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Abstract page for arXiv paper 2312.05033v1: Insomnia impairs muscle function via regulating protein degradation and muscle clock


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Background: Insomnia makes people more physically unable of doing daily duties, which results in a lack of strength, leads to lacking in strength. However, the effects of insomnia on muscle function have not yet been thoroughly investigated. So, the objectives of this study were to clarify how insomnia contributes to the decrease of muscular function and to investigate the mechanisms behind this phenomenon. Methods: To understand how insomnia influence muscle function, we analyzed the expression level of factors associated with muscle protein degradation, muscle protein synthesis , protein synthesis and degradation pathways and muscle clock. Results: The results showed that lower BMI and grip strength were observed in insomnia patients. The mice in the sleep deprivation(SD) group saw a 7.01 g loss in body mass. The SD group's tibialis anterior and gastrocnemius muscle mass decreased after 96 h of SD). The grip strength reduced in SD group. Using the RT-PCR approaches, we found a significant increase in muscle degradation factors expression in SD group versus normal control group. Conclusions: Insomnia can impair muscle function. The mechanism may be associated with the increased expression of muscle degradation related factors , as well as the abnormal expression of Clock gene.


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Abstract page for arXiv paper 2410.16169v1: The Interplay Between Physical Activity, Protein Consumption, and Sleep Quality in Muscle Protein Synthesis


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The Interplay Between Physical Activity, Protein Consumption, and Sleep Quality in Muscle Protein Synthesis


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This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results.


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Abstract page for arXiv paper 2106.05161v1: Interactive Modelling of Volumetric Musculoskeletal Anatomy


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We present a new approach for modelling musculoskeletal anatomy. Unlike previous methods, we do not model individual muscle shapes as geometric primitives (polygonal meshes, NURBS etc.). Instead, we adopt a volumetric segmentation approach where every point in our volume is assigned to a muscle, fat, or bone tissue. We provide an interactive modelling tool where the user controls the segmentation via muscle curves and we visualize the muscle shapes using volumetric rendering. Muscle curves enable intuitive yet powerful control over the muscle shapes. This representation allows us to automatically handle intersections between different tissues (musclemuscle, muscle-bone, and muscle-skin) during the modelling and automates computation of muscle fiber fields. We further introduce a novel algorithm for converting the volumetric muscle representation into tetrahedral or surface geometry for use in downstream tasks. Additionally, we introduce an interactive skeleton authoring tool that allows the users to create skeletal anatomy starting from only a skin mesh using a library of bone parts.


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Abstract page for arXiv paper 2501.05533v1: Propensity score matching in semaglutide retrospective studies


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Propensity Score Matching (PSM) is a causal inference technique that is used as a substitution for experimental methods when it is not possible to implement them due to logistical and ethical concerns. By using a logistic classifier to calculate the probability of assignment between the control and experimental groups a log odds value or 'logit' score is assigned to each data point. After assignment of a logit score every data point in the treatment group is assigned a comparable control in order to balance the potential confounding variables of an experiment. While a viable inference technique, many implementations of PSM fail to properly outline the methodology used, such as not explaining feature selection and matching techniques. This paper outlines multiple different techniques for both feature selection and matching which then are compared based on their efficiency. Three unique quantitative feature selection methods were utilized including random removal, feature importance calculation, and individual removal. Individual removal was the most efficient in consolidating the overlap between the treatment and control groups. The matching techniques used were bisect, binary insertion, nearest neighbors, and the most efficient, nearest neighbor with a caliper, in order to limit the error percentage and standard mean deviation. Only testing these techniques on a data set that included patients treated with semaglutide makes it not possible to definitively state which technique is the best. However, this paper explores the influence of methodology on the outcome of an experiment while providing ways in which to test efficiency of techniques. It is not only important for researchers to properly document methodology but explore different techniques to maximize results.


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Glucagon-like peptide-1 receptor agonists (GLP-1 RA) are novel antihyperglycemic agents. By acting through the central nervous system, they increase satiety and reduce food intake, thus lowering body weight. Furthermore, they increase the secretion of insulin while decreasing the production of glucagon. However, recent studies suggest a more complex metabolic impact through the interaction with various other tissues. In our present review, we aim to provide a summary of the effects of GLP-1 RA on serum lipids, adipose tissue, and muscle metabolism. It has been found that GLP-1 RA therapy is associated with decreased serum cholesterol levels. Epicardial adipose tissue thickness, hepatic lipid droplets, and visceral fat volume were reduced in obese patients with cardiovascular disease. GLP-1 RA therapy decreased the level of proinflammatory adipokines and reduced the expression of inflammatory genes. They have been found to reduce endoplasmic reticulum stress in adipocytes, leading to better adipocyte function and metabolism. Furthermore, GLP-1 RA therapy increased microvascular blood flow in muscle tissue, resulting in increased myocyte metabolism. They inhibited muscle atrophy and increased muscle mass and function. It was also observed that the levels of muscle-derived inflammatory cytokines decreased, and insulin sensitivity increased, resulting in improved metabolism. However, some clinical trials have been conducted on a very small number of patients, which limits the strength of these observations.


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Impact of Selected Glucagon-like Peptide-1 Receptor Agonists on Serum Lipids, Adipose Tissue, and Muscle Metabolism—A Narrative Review


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https://pub.mdpi-res.com/ijms/ijms-25-08214/article_deploy/html/images/ijms-25-08214-ag-550.jpg?1732259049


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Considering the high prevalence of obesity and related metabolic impairments in the population, the unique role nutrition has in weight loss, reversing met


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Nutritional Considerations During Major Weight Loss Therapy: Focus on Optimal Protein and a Low-Carbohydrate Dietary Pattern - Current Nutrition Reports


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Purpose of Review Considering the high prevalence of obesity and related metabolic impairments in the population, the unique role nutrition has in weight loss, reversing metabolic disorders, and maintaining health cannot be overstated. Normal weight and well-being are compatible with varying dietary patterns, but for the last half century there has been a strong emphasis on low-fat, low-saturated fat, high-carbohydrate based approaches. Whereas low-fat dietary patterns can be effective for a subset of individuals, we now have a population where the vast majority of adults have excess adiposity and some degree of metabolic impairment. We are also entering a new era with greater access to bariatric surgery and approval of anti-obesity medications (glucagon-like peptide-1 analogues) that produce substantial weight loss for many people, but there are concerns about disproportionate loss of lean mass and nutritional deficiencies. Recent Findings No matter the approach used to achieve major weight loss, careful attention to nutritional considerations is necessary. Here, we examine the recent findings regarding the importance of adequate protein to maintain lean mass, the rationale and evidence supporting low-carbohydrate and ketogenic dietary patterns, and the potential benefits of including exercise training in the context of major weight loss. Summary While losing and sustaining weight loss has proven challenging, we are optimistic that application of emerging nutrition science, particularly personalized well-formulated low-carbohydrate dietary patterns that contain adequate protein (1.2 to 2.0 g per kilogram reference weight) and achieve the beneficial metabolic state of euketonemia (circulating ketones 0.5 to 5 mM), is a promising path for many individuals with excess adiposity. Graphical Abstract Created with Biorender.com .


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https://static-content.springer.com/image/art%3A10.1007%2Fs13668-024-00548-6/MediaObjects/13668_2024_548_Figa_HTML.png


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The concern with semaglutide is the potential to not only promote fat loss but also inadvertently contribute to muscle wasting or loss.


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Abstract page for arXiv paper 2204.13345v1: The management, treatment and study of skeletal pain harnessing tissue engineering models


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Bone pain typically occurs immediately following skeletal damage with mechanical distortion or rupture of nociceptive fibres. The pain mechanism is also associated with chronic pain conditions where the healing process is impaired. Any load impacting on the area of the fractured bone will increase the intraosseous pressure, consequently stimulating the nociceptive response, necessitating rapid clinical intervention to relieve pain associated with the bone damage and appropriate mitigation of any processes involved with the loss of bone mass, muscle, mobility and, to prevent death. The following review has examined the mechanisms of pain associated with trauma or cancer-related skeletal damage focusing on new approaches for the development of innovative therapeutic interventions. In particular, the current review highlights tissue engineering approaches that offer considerable promise in the application of functional biomimetic fabrication of bone and nerve tissues, resembling the pathological micro-environment. The strategic combination of bone and nerve tissue engineered models provides significant potential to develop a new class of in vitro platforms, capable of replacing in vivo models and testing the safety and efficacy of novel drug treatments aimed at the resolution of bone-associated pain. To date, the field of bone pain research has centred on animal models, with accompanying disparity within models often noted and a paucity of data correlating to the human physiological response. This review explores the evident gap in pain drug development research and suggests a step change in approach to harness tissue engineering technologies to recapitulate the complex pathophysiological environment of the damaged bone tissue enabling evaluation of the associated pain-mimicking mechanism with, ultimately, significant therapeutic potential therein for improved patient quality of life.


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Abstract page for arXiv paper 2001.01139v1: The energy of muscle contraction. I. Tissue force and deformation during isometric contractions


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During contraction the energy of muscle tissue increases due to energy from the hydrolysis of ATP. This energy is distributed across the tissue as strain-energy potentials in the contractile elements, strain-energy potential from the 3D deformation of the base-material tissue (containing cellular and ECM effects), energy related to changes in the muscle's nearly incompressible volume and external work done at the muscle surface. Thus, energy is redistributed through the muscle's tissue as it contracts, with only a component of this energy being used to do mechanical work and develop forces in the muscle's line-of-action. Understanding how the strain-energy potentials are redistributed through the muscle tissue will help enlighten why the mechanical performance of whole muscle in its line-of-action does not match the performance that would be expected from the contractile elements alone. Here we demonstrate these physical effects using a 3D muscle model based on the finite element method. The tissue deformations within contracting muscle are large, and so the mechanics of contraction were explained using the principles of continuum mechanics for large deformations. We present simulations of a contracting medial gastrocnemius muscle, showing tissue deformations that mirror observations from MRI-based images. This paper tracks the redistribution of strain-energy potentials through the muscle tissue during isometric contractions, and shows how fibre shortening, pennation angle, transverse bulging and anisotropy in the stress and strain of the muscle tissue are all related to the interaction between the material properties of the muscle and the action of the contractile elements.


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Abstract page for arXiv paper 0708.1794v1: Systems level circuit model of C. elegans undulatory locomotion: mathematical modeling and molecular genetics


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Systems level circuit model of C. elegans undulatory locomotion: mathematical modeling and molecular genetics


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To establish the relationship between locomotory behavior and dynamics of neural circuits in the nematode C. elegans we combined molecular and theoretical approaches. In particular, we quantitatively analyzed the motion of C. elegans with defective synaptic GABA and acetylcholine transmission, defective muscle calcium signaling, and defective muscles and cuticle structures, and compared the data with our systems level circuit model. The major experimental findings are: (i) anterior-to-posterior gradients of body bending flex for almost all strains both for forward and backward motion, and for neuronal mutants, also analogous weak gradients of undulatory frequency, (ii) existence of some form of neuromuscular (stretch receptor) feedback, (iii) invariance of neuromuscular wavelength, (iv) biphasic dependence of frequency on synaptic signaling, and (v) decrease of frequency with increase of the muscle time constant. Based on (i) we hypothesize that the Central Pattern Generator (CPG) is located in the head both for forward and backward motion. Points (i) and (ii) are the starting assumptions for our theoretical model, whose dynamical patterns are qualitatively insensitive to the details of the CPG design if stretch receptor feedback is sufficiently strong and slow. The model reveals that stretch receptor coupling in the body wall is critical for generation of the neuromuscular wave. Our model agrees with our behavioral data(iii), (iv), and (v), and with other pertinent published data, e.g., that frequency is an increasing function of muscle gap-junction coupling.


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Abstract page for arXiv paper 2103.09489v1: Bio-Inspired Design of Artificial Striated Muscles Composed of Sarcomere-Like Contraction Units (preprint)


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Bio-Inspired Design of Artificial Striated Muscles Composed of Sarcomere-Like Contraction Units (preprint)


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Biological muscles have always attracted robotics researchers due to their efficient capabilities in compliance, force generation, and mechanical work. Many groups are working on the development of artificial muscles, however, state-of-the-art methods still fall short in performance when compared with their biological counterpart. Muscles with high force output are mostly rigid, whereas traditional soft actuators take much space and are limited in strength and producing displacement. In this work, we aim to find a reasonable trade-off between these features by mimicking the striated structure of skeletal muscles. For that, we designed an artificial pneumatic myofibril composed of multiple contraction units that combine stretchable and inextensible materials. Varying the geometric parameters and the number of units in series provides flexible adjustment of the desired muscle operation. We derived a mathematical model that predicts the relationship between the input pneumatic pressure and the generated output force. A detailed experimental study is conducted to validate the performance of the proposed bio-inspired muscle.


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Abstract page for arXiv paper 1212.1928v1: In the search of molecular signature of sarcopenia in C. elegans


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Age-related muscle decline, a condition referred to as sarcopenia and defined as loss in muscle mass and muscle strength over time, is one of the most pervasive problems of the elderly, such that significant declines in strength and mobility affects essentially every old person We have found that ageing C. elegans body wall muscle undergoes a process remarkably reminiscent of human sarcopenia. Both have mid-life onset and are characterized by progressive loss of sarcomeres and cytoplasmic volume; both are associated with locomotory decline. To extend understanding of this fundamental problem, I surveyed expression of all known muscle related genes to describe a profile of transcriptional changes in muscle that transpires during adult life and ageing. Importantly, the intersection of this dataset with that from ageing flies and some human studies can suggest conserved genes that might impact the process most strongly. Hypotheses I formulate will be used to drive experiments at the bench and perhaps to focus attention for human therapies.


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Abstract page for arXiv paper 2411.00128v1: Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations


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Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth muscle activation data, resulting in a scarcity of datasets. In this work, we address this issue by establishing Muscles in Time (MinT), a large-scale synthetic muscle activation dataset. For the creation of MinT, we enriched existing motion capture datasets by incorporating muscle activation simulations derived from biomechanical human body models using the OpenSim platform, a common approach in biomechanics and human motion research. Starting from simple pose sequences, our pipeline enables us to extract detailed information about the timing of muscle activations within the human musculoskeletal system. Muscles in Time contains over nine hours of simulation data covering 227 subjects and 402 simulated muscle strands. We demonstrate the utility of this dataset by presenting results on neural network-based muscle activation estimation from human pose sequences with two different sequence-to-sequence architectures. Data and code are provided under https://simplexsigil.github.io/mint.


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Abstract page for arXiv paper 2212.12760v2: Agent-based Modeling and Simulation of Human Muscle For Development of Human Gait Analyzer Application


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Agent-based Modeling and Simulation of Human Muscle For Development of Human Gait Analyzer Application


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Despite the fact that only a small portion of muscles are affected in motion disease and disorders, medical therapies do not distinguish between healthy and unhealthy muscles. In this paper, a method is devised in order to calculate the neural stimuli of the lower body during gait cycle and check if any group of muscles are not acting properly. For this reason, an agent-based model of human muscle is proposed. The agent is able to convert neural stimuli to force generated by the muscle and vice versa. It can be used in many researches including medical education and research and prosthesis development. Then, Boots algorithm is designed based on a biomechanical model of human lower body to do a reverse dynamics of human motion by computing the forces generated by each muscle group. Using the agent-driven model of human muscle and boots algorithm, a user-friendly application is developed which can calculate the number of neural stimuli received by each muscle during gait cycle. The application can be used by clinical experts to distinguish between healthy and unhealthy muscles.


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Abstract page for arXiv paper 1804.05079v1: Robust Estimation of the Weighted Average Treatment Effect for A Target Population


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The weighted average treatment effect (WATE) is a causal measure for the comparison of interventions in a specific target population, which may be different from the population where data are sampled from. For instance, when the goal is to introduce a new treatment to a target population, the question is what efficacy (or effectiveness) can be gained by switching patients from a standard of care (control) to this new treatment, for which the average treatment effect for the control (ATC) estimand can be applied. In this paper, we propose two estimators based on augmented inverse probability weighting to estimate the WATE for a well defined target population (i.e., there exists a target function that describes the population of interest), using observational data. The first proposed estimator is doubly robust if the target function is known or can be correctly specified. The second proposed estimator is doubly robust if the target function has a linear dependence on the propensity score, which can be used to estimate the average treatment effect for the treated (ATT) and ATC. We demonstrate the properties of the proposed estimators through theoretical proof and simulation studies. We also apply our proposed methods in a comparison of glucagon-like peptide-1 receptor agonists therapy and insulin therapy among patients with type 2 diabetes, using the UK clinical practice research datalink data.


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Abstract page for arXiv paper 2010.11124v1: Modeling Suggests Combined-Drug Treatments for Disorders Impairing Synaptic Plasticity via Shared Signaling Pathways


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Modeling Suggests Combined-Drug Treatments for Disorders Impairing Synaptic Plasticity via Shared Signaling Pathways


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Genetic disorders such as Rubinstein-Taybi syndrome (RTS) and Coffin-Lowry syndrome (CLS) cause lifelong cognitive disability, including deficits in learning and memory. Can pharmacological therapies be suggested to improve learning and memory in these disorders? To address this question, we simulated drug effects within a computational model describing induction of late long-term potentiation (L-LTP). Biochemical pathways impaired in these and other disorders converge on a common target, histone acetylation by acetyltransferases such as CREB binding protein (CBP), which facilitates gene induction necessary for L-LTP. We focused on four drug classes: tropomyosin receptor kinase B (TrkB) agonists, cAMP phosphodiesterase inhibitors, histone deacetylase inhibitors, and ampakines. Simulations suggested each drug type alone may rescue deficits in L-LTP. A potential disadvantage, however, was the necessity of simulating strong drug effects (high doses), which could produce adverse side effects. Thus, we investigated the effects of six drug pairs among the four classes described above. These combination treatments normalized impaired L-LTP with substantially smaller drug doses. In addition three of these combinations, a TrkB agonist paired with an ampakine and a cAMP phosphodiesterase inhibitor paired with a TrkB agonist or an ampakine, exhibited strong synergism in L-LTP rescue. Therefore, we suggest these drug combinations are promising candidates for further empirical studies in animal models of genetic disorders that impair acetylation, L-LTP, and learning.


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While muscle mass drops with any kind of weight loss, experts argue that the benefits outweigh the risks in those who need it.


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Abstract page for arXiv paper 1310.8268v2: Cellular decision-making bias: the missing ingredient in cell functional diversity


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Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.


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Sarcopenia, the age-related loss of muscle strength and mass or quality, is a common condition with major adverse consequences. Although the pathophysiolog


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Repurposing Drugs for Diabetes Mellitus as Potential Pharmacological Treatments for Sarcopenia – A Narrative Review - Drugs & Aging


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Sarcopenia, the age-related loss of muscle strength and mass or quality, is a common condition with major adverse consequences. Although the pathophysiology is incompletely understood, there are common mechanisms between sarcopenia and the phenomenon of accelerated ageing seen in diabetes mellitus. Drugs currently used to treat type 2 diabetes mellitus may have mechanisms of action that are relevant to the prevention and treatment of sarcopenia, for those with type 2 diabetes and those without diabetes. This review summarises shared pathophysiology between sarcopenia and diabetes mellitus, including the effects of advanced glycation end products, mitochondrial dysfunction, chronic inflammation and changes to the insulin signalling pathway. Cellular and animal models have generated intriguing, albeit mixed, evidence that supports possible beneficial effects on skeletal muscle function for some classes of drugs used to treat diabetes, including metformin and SGLT2 inhibitors. Most human observational and intervention evidence for the effects of these drugs has been derived from populations with type 2 diabetes mellitus, and there is a need for intervention studies for older people with, and at risk of, sarcopenia to further investigate the balance of benefit and risk in these target populations. Not all diabetes treatments will be safe to use in those without diabetes because of variable side effects across classes. However, some agents [including glucagon-like peptide (GLP)-1 receptor agonists and SGLT2 inhibitors] have already demonstrated benefits in populations without diabetes, and it is these agents, along with metformin, that hold out the most promise for further investigation in sarcopenia.


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https://static-content.springer.com/image/art%3A10.1007%2Fs40266-023-01042-4/MediaObjects/40266_2023_1042_Fig1_HTML.png


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Steven Asch is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). The site facilitates research and collaboration in academic endeavors.


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Abstract page for arXiv paper 2305.07647v1: A Causal Roadmap for Hybrid Randomized and Real-World Data Designs: Case Study of Semaglutide and Cardiovascular Outcomes


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A Causal Roadmap for Hybrid Randomized and Real-World Data Designs: Case Study of Semaglutide and Cardiovascular Outcomes


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Introduction: Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: 1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), 2) a single RCT, and 3) a hybrid randomized-external data study. Methods: The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient-time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis. Results: In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient-time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of -1.53%-points (95% CI -2.75%-points to -0.30%-points). Conclusions: The Causal Roadmap helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction.


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Abstract page for arXiv paper 2405.04707v1: A wearable anti-gravity supplement to therapy does not improve arm function in chronic stroke: a randomized trial


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A wearable anti-gravity supplement to therapy does not improve arm function in chronic stroke: a randomized trial


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Background: Gravity confounds arm movement ability in post-stroke hemiparesis. Reducing its influence allows effective practice leading to recovery. Yet, there is a scarcity of wearable devices suitable for personalized use across diverse therapeutic activities in the clinic. Objective: In this study, we investigated the safety, feasibility, and efficacy of anti-gravity therapy using the ExoNET device in post-stroke participants. Methods: Twenty chronic stroke survivors underwent six, 45-minute occupational therapy sessions while wearing the ExoNET, randomized into either the treatment (ExoNET tuned to gravity-support) or control group (ExoNET tuned to slack condition). Clinical outcomes were evaluated by a blinded-rater at baseline, post, and six-week follow-up sessions. Kinetic, kinematic, and patient experience outcomes were also assessed. Results: Mixed-effect models showed a significant improvement in Box and Blocks scores in the post-intervention session for the treatment group (effect size: 2.1, p = .04). No significant effects were found between the treatment and control groups for ARAT scores and other clinical metrics. Direct kinetic effects revealed a significant reduction in muscle activity during free exploration with an effect size of (-7.12%, p< 005). There were no significant longitudinal kinetic or kinematic trends. Subject feedback suggested a generally positive perception of the anti-gravity therapy. Conclusions: Anti-gravity therapy with the ExoNET is a safe and feasible treatment for post-stroke rehabilitation. The device provided anti-gravity forces, did not encumber range of motion, and clinical metrics of anti-gravity therapy demonstrated improvements in gross manual dexterity. Further research is required to explore potential benefits in broader clinical metrics.


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Abstract page for arXiv paper 2502.09088v1: Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection


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Sarcopenia is an age-related progressive loss of muscle mass and strength that significantly impacts daily life. A commonly studied criterion for characterizing the muscle mass has been the combination of 3D imaging and manual segmentations. In this paper, we instead study the muscles' shape. We rely on an implicit neural representation (INR) to model normal muscle shapes. We then introduce an unsupervised anomaly detection method to identify sarcopenic muscles based on the reconstruction error of the implicit model. Relying on a conditional INR with an auto-decoding strategy, we also learn a latent representation of the muscles that clearly separates normal from abnormal muscles in an unsupervised fashion. Experimental results on a dataset of 103 segmented volumes indicate that our double anomaly detection strategy effectively discriminates sarcopenic and non-sarcopenic muscles.


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Abstract page for arXiv paper 2309.05863v1: The bionic neural network for external simulation of human locomotor system


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Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models can interpret the dynamic interaction between the neural drive to muscles, muscle dynamics, body and joint kinematics, and kinetics. Still, such a set of solutions suffers from high computational time and muscle recruitment problems, especially in complex modeling. In recent years, data-driven methods have emerged as a promising alternative due to the benefits of flexibility and adaptability. However, a large amount of labeled training data is not easy to be acquired. This paper proposes a physics-informed deep learning method based on MSK modeling to predict joint motion and muscle forces. The MSK model is embedded into the neural network as an ordinary differential equation (ODE) loss function with physiological parameters of muscle activation dynamics and muscle contraction dynamics to be identified. These parameters are automatically estimated during the training process which guides the prediction of muscle forces combined with the MSK forward dynamics model. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The results demonstrate that the proposed deep learning method can effectively identify subject-specific MSK physiological parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion and muscle forces predictions.


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Abstract page for arXiv paper 2310.20355v1: Muscle volume quantification: guiding transformers with anatomical priors


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Muscle volume is a useful quantitative biomarker in sports, but also for the follow-up of degenerative musculo-skelletal diseases. In addition to volume, other shape biomarkers can be extracted by segmenting the muscles of interest from medical images. Manual segmentation is still today the gold standard for such measurements despite being very time-consuming. We propose a method for automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance Images to assist such morphometric analysis. By their nature, the tissue of different muscles is undistinguishable when observed in MR Images. Thus, muscle segmentation algorithms cannot rely on appearance but only on contour cues. However, such contours are hard to detect and their thickness varies across subjects. To cope with the above challenges, we propose a segmentation approach based on a hybrid architecture, combining convolutional and visual transformer blocks. We investigate for the first time the behaviour of such hybrid architectures in the context of muscle segmentation for shape analysis. Considering the consistent anatomical muscle configuration, we rely on transformer blocks to capture the longrange relations between the muscles. To further exploit the anatomical priors, a second contribution of this work consists in adding a regularisation loss based on an adjacency matrix of plausible muscle neighbourhoods estimated from the training data. Our experimental results on a unique database of elite athletes show it is possible to train complex hybrid models from a relatively small database of large volumes, while the anatomical prior regularisation favours better predictions.


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Abstract page for arXiv paper 2308.07456v1: Efficacy of mannan-oligosaccharide and live yeast feed additives on performance, rumen morphology, serum biochemical parameters and muscle morphometric characteristics in buffalo calves


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Efficacy of mannan-oligosaccharide and live yeast feed additives on performance, rumen morphology, serum biochemical parameters and muscle morphometric characteristics in buffalo calves


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The objective of the current study was to assess the effect of dietary supplementations of mannan-oligosaccharide, live yeast, and a combination of these two additives on growth performance, histo-morphology of the rumen, and muscle morphometric attributes in buffalo calves. A total of twenty buffalo calves (average weight of 25 kg) having 3 months of age were distributed according to a complete randomized design. All animals were individually stalled in the shed and were fed ad-libitum. Experimental animals were divided into four groups for 67 days: Control group(without the inclusion of dietary supplementation); MOS group (Mannan oligosaccharide 5 g/clave/day; Yeast group (Live yeast 2g/calve/day) and Mixed group (MOS + Live Yeast 2.5g + 1g )/calve/day. Experimental results revealed that combined supplementation of MOS and Yeast and MOS alone resulted in an increased number of short-chain fatty acids in the rumen as well as ruminal pH (P<0.05). Results showed a significant improvement in average daily gain and FCR of MOS and Mixed supplemented groups (P<0.05). Histomorphological evaluation of rumen mucosal epithelium showed a significant improvement in the mixed-supplemented group (P<0.05) as compared to the yeast-supplemented and control groups. Muscle quality parameters such as meat texture showed significant improvement in MOS and mix-supplemented groups. Histological examination of longissimus dorsi muscle cross-section showed a significantly higher(P<0.05) muscle fiber and muscle fascicle diameter in both MOS and mix-supplemented calves groups. In conclusion, the results of this experiment revealed that the dietary addition of MOS, Live yeast, and their combination have positive effects on growth performance, rumen histology indices, and muscle morphometric features in buffalo calves.


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Abstract page for arXiv paper 2408.02547v1: The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces


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The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces


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Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface electromyography (sEMG) has been explored for its rich informational context and accessibility when combined with advanced machine learning approaches and wearable systems. The literature presents numerous approaches to boost performance while ensuring robustness for neurorobots using sEMG, often resulting in models requiring high processing power, large datasets, and less scalable solutions. This paper addresses this challenge by proposing the decoding of muscle synchronization rather than individual muscle activation. We study coherence-based functional muscle networks as the core of our perception model, proposing that functional synchronization between muscles and the graph-based network of muscle connectivity encode contextual information about intended hand gestures. This can be decoded using shallow machine learning approaches without the need for deep temporal networks. Our technique could impact myoelectric control of neurorobots by reducing computational burdens and enhancing efficiency. The approach is benchmarked on the Ninapro database, which contains 12 EMG signals from 40 subjects performing 17 hand gestures. It achieves an accuracy of 85.1%, demonstrating improved performance compared to existing methods while requiring much less computational power. The results support the hypothesis that a coherence-based functional muscle network encodes critical information related to gesture execution, significantly enhancing hand gesture perception with potential applications for neurorobotic systems and interactive machines.


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Diet and lifestyle modifications remain the foundation of obesity treatment, but they have historically proven insufficient for significant, long-term weight loss. As a result, there is a high demand for new pharmacologic treatments to promote ...


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Although good glycemic control in patients with type 2 diabetes (T2D) can prevent cardiovascular complications, many diabetic patients still have poor optimal control. A new class of antidiabetic drugs (e.g., glucagon-like peptide-1-GLP-1 receptor agonists, sodium-glucose co-transporters-SGLT2 inhibitors), in addition to the low hypoglycemic effect, exert multiple beneficial effects at a metabolic and cardiovascular level, through mechanisms other than antihyperglycemic agents. This review aims to discuss the effects of these new antidiabetic drugs, highlighting cardiovascular and metabolic benefits, through the description of their action mechanisms as well as available data by preclinical and clinical studies. Moreover, new innovative tools in the T2D field will be described which may help to advance towards a better targeted T2D personalized care in future.


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https://pub.mdpi-res.com/ijms/ijms-25-06218/article_deploy/html/images/ijms-25-06218-ag-550.jpg?1717594016


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The glucagon-like peptide-1 (GLP-1) is a multifaceted hormone with broad pharmacological potential. Among the numerous metabolic effects of GLP-1 are the glucose-dependent stimulation of insulin secretion, decrease of gastric emptying, inhibition of ...


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Diet and lifestyle modifications remain the foundation of obesity treatment, but they have historica


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ociated with obesity. Researchers are particularly interested in 1 type of drug, glucagon-like peptide 1 receptor agonists (GLP-1 RAs), because of its promising potential in addressing the limitations of non-pharmacologic treatments. In addition to their role in weight loss, these drugs have shown promising early evidence of cardiovascular benefits in obese patients, further enhancing their clinical relevance. Semaglutide and liraglutide, which were initially approved for the treatment of type 2 diabetes, have since been approved by the Food and Drug Administration as weight loss medications due to their effectiveness in promoting significant and sustained weight loss. In this narrative review, we will explore the mechanism of GLP-1 RAs, their effects on weight loss, cardiovascular risk factors and outcomes, common adverse effects, and strategies for managing these effects....


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Is there compelling evidence that those who lose weight with GLP-1 receptor agonist or GLP-1/GIP medications lose more skeletal muscle or more strength than do those who lose equivalent weight without a GLP-1?


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https://imgctf--assets.ro.co/jj2wf7627pjc/3BY5QpyVKJfAfOQk9Ac7HD/da04425f33763319b0a1cf3d57c20cc4/share__3_.png?fm=jpg&w=1200


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When it comes to data on GLP-1 drugs' effects on lean body mass, the data remains less than clear at present.


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https://www.drugdiscoverytrends.com/wp-content/uploads/2023/10/AdobeStock_108090571-scaled-e1696291303980.jpeg


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Type 1 diabetes mellitus (T1D) is a chronic autoimmune disease caused by the immune-mediated destruction of insulin-producing pancreatic beta cells, resulting in the lifelong need for exogenous insulin. Over the last few years, overweight and obesity have recently emerged as growing health issues also afflicting patients with T1D. In this context, the term “double diabetes” has been coined to indicate patients with T1D who have a family history of type 2 diabetes mellitus (T2D) and/or patients with T1D who are affected by insulin resistance and/or overweight/obesity and/or metabolic syndrome. At the same time, the use of second-generation incretin analogs semaglutide and tirzepatide has substantially increased on a global scale over the last few years, given the remarkable clinical benefits of these drugs (in terms of glucose control and weight loss) in patients with T2D and/or overweight/obesity. Although the glucagon-like peptide-1 (GLP-1) receptor agonists and the novel dual GIP (glucose-dependent insulinotropic polypeptide)/GLP-1 receptor agonist tirzepatide are currently not approved for the treatment of T1D, a growing body of evidence over the last few years has shown that these medications may serve as valid add-on treatments to insulin with substantial efficacy in improving glucose control, promoting weight loss, preserving residual beta-cell function and providing other beneficial metabolic effects in patients with T1D, double diabetes and latent autoimmune diabetes in adults (LADA). This manuscript aims to comprehensively review the currently available literature (mostly consisting of real-world studies) regarding the safety and therapeutic use (for different purposes) of semaglutide and tirzepatide in patients with T1D (at different stages of the disease), double diabetes and LADA.


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https://pub.mdpi-res.com/jcm/jcm-14-01303/article_deploy/html/images/jcm-14-01303-g001-550.jpg?1740486294


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Following myocardial infarction (MI), reperfusion of coronary flow is necessary to rescue the ischemic myocardium, limit infarct size and improve prognosis. Paradoxically, reperfusion results in additional ‘ischemia-reperfusion injury’ (IRI), accounting for ~50% of infarct size. Despite several Phase II/III trials of candidate treatments, there remains no approved therapy against IRI. Recently two anti-diabetic drug classes, the sodium-glucose co-transporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) have independently demonstrated cardiovascular benefits in subjects with and without type 2 diabetes (T2D). However, it is not known whether the cardiovascular effects of SGLT2i and GLP-1RA are through shared or distinct mechanisms, and whether combining these agents would result in additive effects. Thus, this Dissertation examines whether combined treatment with SGLT2i dapagliflozin (Dapa) and GLP-1RA exenatide (Ex-4) would be more cardioprotective than treatment with either agent alone, in non-diabetic and diabetic mouse models of IRI. 
Chapter 3 demonstrates combined pre-treatment with Dapa+Ex-4 better protects against infarct remodeling in a non-diabetic mouse model of IRI, than treatment with Dapa alone. Mice pre-treated with Dapa+Ex-4 exhibited the greatest reductions infarct size and improvements in ejection fraction (EF) on d-28 post-IRI. Cardiac proteome/ phosphoproteome and systemic metabolome analyses demonstrated distinct enrichment of mitochondrial electron transport chain metabolism with Dapa+Ex-4 treatment. The effects of in vivo treatment extended to isolated cardiomyocytes which demonstrated enhanced aerobic respiration uniquely following Dapa+Ex-4 treatment—plausibly contributing to the greater cardioprotective effects of combination therapy.
Chapter 4 demonstrates pre-treatment with Dapa+Ex-4 preserves EF to a similar extent as single agent, in an obesity-induced diabetic mouse model of IRI. However, pressure-volume loop analysis revealed improved cardiac loading conditions exclusively with Dapa+Ex-4 treatment. Systemic effects of in vivo treatment and T2D-induced metabolic disturbances characterized by metabolomics, once again extended to the myocardium, as isolated cardiomyocytes exhibited increased aerobic metabolism accompanied (and plausibly blunted in benefit) by elevated glycolytic metabolism uniquely following Dapa+Ex-4 treatment.
Taken together, this Dissertation begins to elucidate the complex cardioprotective actions of combined SGLT2i+GLP-1RA in IRI with or without T2D, and provides rationale for their combined use in a condition (IRI) for which there remains no approved therapy.


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Cardiovascular disease (CVD) and kidney disease are the main causes of morbidity and mortality in type 2 diabetes mellitus (T2DM). Globally, the incidence of T2DM continues to rise. A substantial increase in the burden of CVD and renal disease, alongside the socioeconomic implications, would be anticipated. Adopting a purely glucose-centric approach focusing only on glycemic targets is no longer adequate to mitigate the cardiovascular risks in T2DM. In the past decade, significant advancement has been achieved in expanding the pharmaceutical options for T2DM, with novel agents such as the sodium-glucose cotransporter type 2 (SGLT2) inhibitors and glucagon-like peptide receptor agonists (GLP-1 RAs) demonstrating robust evidence in cardiorenal protection. Combinatorial approaches comprising multiple pharmacotherapies combined in a single agent are an emerging and promising way to not only enhance patient adherence and improve glycemic control but also to achieve the potential synergistic effects for greater cardiorenal protection. In this review, we provide an update on the novel antidiabetic agents in the past decade, with an appraisal of the mechanisms contributing to cardiorenal protection. Additionally, we offer a glimpse into the landscape of T2DM management in the near future by providing a comprehensive summary of upcoming agents in early-phase trials.


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Novel Therapeutics for Type 2 Diabetes Mellitus—A Look at the Past Decade and a Glimpse into the Future


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https://pub.mdpi-res.com/biomedicines/biomedicines-12-01386/article_deploy/html/images/biomedicines-12-01386-g001-550.jpg?1720686806


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Diabetes medications can affect weight and cardiovascular health. Some medications can aid in weight management, while others may lead to weight gain. Patients must be monitored and receive appropriate care to manage weight and prevent cardiovascular complications. Despite advancements in diabetes treatments that can influence weight and cardiovascular outcomes, ongoing research is necessary in this intricate field. Long-term effects, individual variations, and combination therapies are still subjects of uncertainty and ongoing investigation. The major objective of the research is to evaluate the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) on body weight in diabetic patients through a scientometric assessment. Methodology: Research data were gathered from the Web of Science Core Collection (WoSCC) database by searching for the keywords “Body Weight”, dulaglutide, and semaglutide, identifying 60 relevant articles in the field. While there are advantages in managing diseases in which the cardiovascular system is implicated, there are also clinical considerations for personalized medicine and shared decision-making. The scientometric analysis of the articles revealed important insights into how dulaglutide and semaglutide impact weight management and their potential benefits for managing cardiovascular diseases in individuals with diabetes. Conclusions: Semaglutide shows superior outcomes compared to other commercially available GLP-1RAs, particularly in improving blood sugar control, lowering body weight, and addressing other cardio-metabolic risk factors in individuals with type 2 diabetes (T2DM). The findings suggest that GLP-1 RAs have the potential to provide cardiovascular protection by influencing various physiological factors such as blood pressure, pulse rate, glycated hemoglobin (HbA1c) levels, and the urinary albumin-to-creatinine ratio (RAC). The development and validation of the 4GI model provides a sophisticated tool for evaluating the complex interactions involved in diabetes treatments, offering insights into the mechanisms of action of various medications.


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The Influence of GLP1 on Body Weight and Glycemic Management in Patients with Diabetes—A Scientometric Investigation and Visualization Study


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https://pub.mdpi-res.com/medicina/medicina-60-01761/article_deploy/html/images/medicina-60-01761-g001-550.jpg?1730002698


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Diabetes is a global disease that can lead to a range of complications. Currently, the treatment of type 2 diabetes focuses on oral hypoglycemic drugs and insulin analogues. Studies have shown that drugs such as oral metformin are useful in the treatment of diabetes but can limit the liver’s ability to release sugar. The development of glucose-lowering peptides has provided new options for the treatment of type 2 diabetes. Peptide drugs have low oral utilization due to their easy degradation, short half-life, and difficulty passing through the intestinal mucosa. Therefore, improving the oral utilization of peptide drugs remains an urgent problem. This paper reviews the research progress of peptide drugs in the treatment of diabetes mellitus and proposes that different types of nano-formulation carriers, such as liposomes, self-emulsifying drug delivery systems, and polymer particles, should be combined with peptide drugs for oral administration to improve their absorption in the gastrointestinal tract.


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https://pub.mdpi-res.com/pharmaceutics/pharmaceutics-16-01353/article_deploy/html/images/pharmaceutics-16-01353-g001-550.jpg?1729687607


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Background: Anabolic resistance accelerates muscle loss in aging and obesity, thus predisposing to sarcopenic obesity. Methods: In this retrospective analysis of a randomized clinical trial, we examined baseline predictors of the adaptive response to three months of home-based resistance exercise, daily physical activity, and protein-based, multi-ingredient supplementation (MIS) in a cohort of free-living, older males (n = 32). Results: Multiple linear regression analyses revealed that obesity and a Global Risk Index for metabolic syndrome (MetS) were the strongest predictors of Δ% gains in lean mass (TLM and ASM), LM/body fat ratios (TLM/%BF, ASM/FM, and ASM/%BF), and allometric LM (ASMI, TLM/BW, TLM/BMI, ASM/BW), with moderately strong, negative correlations to the adaptive response to polytherapy r = −0.36 to −0.68 (p < 0.05). Kidney function, PA level, and chronological age were only weakly associated with treatment outcomes (p > 0.05). Next, we performed a subgroup analysis in overweight/obese participants with at least one other MetS risk factor and examined their adaptive response to polytherapy with two types of protein-based MIS (PLA; collagen peptides and safflower oil, n = 8, M5; whey/casein, creatine, calcium, vitamin D3, and fish oil, n = 12). The M5 group showed greater improvements in LM (ASM; +2% vs. −0.8%), LM/body fat ratios (ASM/FM; +3.8% vs. −5.1%), allometric LM (ASM/BMI; +1.2% vs. −2.5%), strength (leg press; +17% vs. −1.4%), and performance (4-Step-Stair-Climb time; −10.5% vs. +1.1%) vs. the PLA group (p < 0.05). Bone turnover markers, indicative of bone accretion, were increased pre-to-post intervention in the M5 group only (P1NP; p = 0.036, P1NP/CTX ratio; p = 0.088). The overall anabolic response, as indicated by ranking low-to-high responders for Δ% LM (p = 0.0079), strength (p = 0.097), and performance (p = 0.19), was therefore significantly higher in the M5 vs. PLA group (p = 0.013). Conclusions: Our findings confirm that obesity/MetS is a key driver of anabolic resistance in old age and that a high-quality, whey/casein-based MIS is more effective than a collagen-based alternative for maintaining musculoskeletal health in individuals at risk for sarcopenic obesity, even when total daily protein intake exceeds current treatment guidelines.


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https://pub.mdpi-res.com/nutrients/nutrients-16-04407/article_deploy/html/images/nutrients-16-04407-ag-550.jpg?1735201185


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Diabetes is one of the chronic metabolic disorders which poses a multitude of life-debilitating challenges, including cardiac muscle impairment, which eventually results in heart failure. The incretin hormone glucagon-like peptide-1 (GLP-1) has gained distinct recognition in reinstating glucose homeostasis in diabetes, while it is now largely accepted that it has an array of biological effects in the body. Several lines of evidence have revealed that GLP-1 and its analogs possess cardioprotective effects by various mechanisms related to cardiac contractility, myocardial glucose uptake, cardiac oxidative stress and ischemia/reperfusion injury, and mitochondrial homeostasis. Upon binding to GLP-1 receptor (GLP-1R), GLP-1 and its analogs exert their effects via adenylyl cyclase-mediated cAMP elevation and subsequent activation of cAMP-dependent protein kinase(s) which stimulates the insulin release in conjunction with enhanced Ca2+ and ATP levels. Recent findings have suggested additional downstream molecular pathways stirred by long-term exposure of GLP-1 analogs, which pave the way for the development of potential therapeutic molecules with longer lasting beneficial effects against diabetic cardiomyopathies. This review provides a comprehensive overview of the recent advances in the understanding of the GLP-1R-dependent and -independent actions of GLP-1 and its analogs in the protection against cardiomyopathies.


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https://pub.mdpi-res.com/pharmaceuticals/pharmaceuticals-16-00836/article_deploy/html/images/pharmaceuticals-16-00836-g001-550.jpg?1685936918


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TG103, a glucagon-like peptide-1 analog, is being investigated as an option for weight management. We aimed to determine the safety, tolerability, pharmacokinetics, and pharmacodynamics of TG103 injection in participants who are overweight or obese without diabetes. In this randomized, double-blind, placebo-controlled, multiple-dose phase 1b study, participants aged 18–75&nbsp;years with a body-mass index (BMI) ≥ 26.0&nbsp;kg/m2 and body weight ≥ 60&nbsp;kg were enrolled from three centers in China. The study included three cohorts, and in each cohort, eligible participants were randomly assigned (3:1) to one of three once-weekly subcutaneous TG103 groups (15.0, 22.5 and 30.0&nbsp;mg) or matched placebo, without lifestyle interventions. In each cohort, the doses of TG103 were escalated in 1-week intervals to the desired dose over 1 to 4&nbsp;weeks. Then participants were treated at the target dose until week 12 and then followed up for 2&nbsp;weeks. The primary endpoint was safety and tolerability assessed by the incidence and severity of adverse events (AEs) from baseline to the end of the follow-up period. Secondary endpoints included pharmacokinetic and pharmacodynamic profiles of TG103 and the occurrence of anti-drug antibodies to TG103. A total of 147 participants were screened, and 48 participants were randomly assigned to TG103 (15.0, 22.5 and 30.0&nbsp;mg groups, n = 12 per group) or placebo (n = 12). The mean (standard deviation, SD) age of the participants was 33.9 (10.0) years; the mean bodyweight was 81.65 (10.50) kg, and the mean BMI was 29.8 (2.5) kg/m2. A total of 466 AEs occurred in 45 of the 48 participants, with 35 (97.2%) in the TG103 group and 10 (83.3%) in the pooled placebo group. Most AEs were grade 1 or 2 in severity, and there were no serious adverse events (SAEs), AEs leading to death, or AEs leading to discontinuation of treatment. The steady-state exposure of TG103 increased with increasing dose and was proportional to Cmax,ss, AUCss, AUC0-t and AUC0-inf. The mean values of Cmax,ss ranged from 951 to 1690&nbsp;ng/mL, AUC0-t ranged from 150 to 321&nbsp;μg*h/mL, and AUC0-inf ranged from 159 to 340&nbsp;μg*h/mL. TG103 had a half-life of 110–116&nbsp;h, with a median Tmax of 36–48&nbsp;h. After treatment for 12&nbsp;weeks, the mean (SD) values of weight loss from baseline in the TG103 15.0&nbsp;mg, 22.5&nbsp;mg and 30.0&nbsp;mg groups were 5.65 (3.30) kg, 5.35 (3.39) kg and 5.13 (2.56) kg, respectively, and that in the placebo group was 1.37 (2.13) kg. The least square mean percent weight loss from baseline to D85 in all the TG103 groups was more than 5% with p &lt; 0.05 for all comparisons with placebo. In this trial, all three doses of once-weekly TG103 were well tolerated with an acceptable safety profile. TG103 demonstrated preliminary 12-week body weight loss without lifestyle interventions, thus showing great potential for the treatment of overweight and obesity. ClinicalTrials.gov, NCT04855292. Registered on April 22, 2021.


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Safety, tolerability, pharmacokinetics, and pharmacodynamics of TG103 injection in participants who are overweight or obese: a randomized, double-blind, placebo-controlled, multiple-dose phase 1b study - BMC Medicine


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Background TG103, a glucagon-like peptide-1 analog, is being investigated as an option for weight management. We aimed to determine the safety, tolerability, pharmacokinetics, and pharmacodynamics of TG103 injection in participants who are overweight or obese without diabetes. Methods In this randomized, double-blind, placebo-controlled, multiple-dose phase 1b study, participants aged 18–75 years with a body-mass index (BMI) ≥ 26.0 kg/m2 and body weight ≥ 60 kg were enrolled from three centers in China. The study included three cohorts, and in each cohort, eligible participants were randomly assigned (3:1) to one of three once-weekly subcutaneous TG103 groups (15.0, 22.5 and 30.0 mg) or matched placebo, without lifestyle interventions. In each cohort, the doses of TG103 were escalated in 1-week intervals to the desired dose over 1 to 4 weeks. Then participants were treated at the target dose until week 12 and then followed up for 2 weeks. The primary endpoint was safety and tolerability assessed by the incidence and severity of adverse events (AEs) from baseline to the end of the follow-up period. Secondary endpoints included pharmacokinetic and pharmacodynamic profiles of TG103 and the occurrence of anti-drug antibodies to TG103. Results A total of 147 participants were screened, and 48 participants were randomly assigned to TG103 (15.0, 22.5 and 30.0 mg groups, n = 12 per group) or placebo (n = 12). The mean (standard deviation, SD) age of the participants was 33.9 (10.0) years; the mean bodyweight was 81.65 (10.50) kg, and the mean BMI was 29.8 (2.5) kg/m2. A total of 466 AEs occurred in 45 of the 48 participants, with 35 (97.2%) in the TG103 group and 10 (83.3%) in the pooled placebo group. Most AEs were grade 1 or 2 in severity, and there were no serious adverse events (SAEs), AEs leading to death, or AEs leading to discontinuation of treatment. The steady-state exposure of TG103 increased with increasing dose and was proportional to Cmax,ss, AUCss, AUC0-t and AUC0-inf. The mean values of Cmax,ss ranged from 951 to 1690 ng/mL, AUC0-t ranged from 150 to 321 μg*h/mL, and AUC0-inf ranged from 159 to 340 μg*h/mL. TG103 had a half-life of 110–116 h, with a median Tmax of 36–48 h. After treatment for 12 weeks, the mean (SD) values of weight loss from baseline in the TG103 15.0 mg, 22.5 mg and 30.0 mg groups were 5.65 (3.30) kg, 5.35 (3.39) kg and 5.13 (2.56) kg, respectively, and that in the placebo group was 1.37 (2.13) kg. The least square mean percent weight loss from baseline to D85 in all the TG103 groups was more than 5% with p < 0.05 for all comparisons with placebo. Conclusions In this trial, all three doses of once-weekly TG103 were well tolerated with an acceptable safety profile. TG103 demonstrated preliminary 12-week body weight loss without lifestyle interventions, thus showing great potential for the treatment of overweight and obesity. Trial registration ClinicalTrials.gov, NCT04855292. Registered on April 22, 2021.


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https://static-content.springer.com/image/art%3A10.1186%2Fs12916-024-03394-z/MediaObjects/12916_2024_3394_Fig1_HTML.png


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There is some debate as to whether GLP-1 agonist drugs like Wegovy can lead to unhealthy muscle mass loss in addition to general weight loss. Is this a real concern, and if so, how can a person maintain their muscle mass on GLP-1 drugs?


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https://media.post.rvohealth.io/wp-content/uploads/sites/3/2024/11/muscle-loss-Stocksy_txp0ea61167Lg2400_Medium_4473836-Thumb.jpg


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Background: As the global prevalence of obesity continues to rise, the health and economic burden of a variety of obesity-related complications is becoming more substantial. A body mass index (BMI) over 25 kg/m2 is considered overweight, and over 30 kg/m2 is used to define obesity. Obesity is a recognized risk factor for many illnesses, such as type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), hypertension, myocardial infarction, and vascular disease, osteoarthritis, multiple cancers, and obstructive sleep apnea (OSA). the development of Obstructive Sleep Apnea (OSA), a widely underdiagnosed condition which impacts significantly patient health and quality of life. In recent years, devices that enable diagnosis and monitoring of OSA in the community have become more popular, given the extensive resource requirements of formal full polysomnography (PSG) testing for OSA. Objective: The first study aimed to determine changes in detailed polysomnography parameters after weight loss surgery in patients with obstructive sleep apnea. The second study aimed to investigated whether liraglutide 3.0 mg would reduce the severity of OSA and other obesity-related complications compared with usual care. The third study aimed to assess the result of bariatric surgery on OSA and other obesity-related complications. Methods: The first study, recruited 5 patients in the Sleep Disorders Unit in St Vincent’s Healthcare Group, Dublin Ireland, between 2019 and 2021. The second study recruited 40 patients from the STRIVE study at St. Vincent’s University Hospital (SVUH), Dublin, between 2019 and 2020 and the third study recruited 24 patients from the bariatric surgery unit at St. Vincent's University Hospital (SVUH), Dublin, from 2018 to 2020 with a BMI of 38.4 to 58.2 kg/m2, before and after laparoscopic sleeve gastrectomy (LSG) or Roux- en-Y gastric bypass (RYGB). In the first study, the 5 patients completed a questionnaire concerning possible daily or nocturnal symptoms, medication, intoxications, and medical history. The Epworth Sleepiness Scale (ESS) was included in the questionnaire. Patients used a scale of 0–3 for each of the questions on the ESS. The scale measured the level of falling asleep in eight different situations, giving an overall score between 0 and 24: the higher the score, the sleepier the individual. We performed overnight polysomnography (PSG) on all 5 patients. PSGs were conducted preoperatively and postoperatively within one year. Embla Sandman, Version 10.1.1, was used to conduct PSGs. For the second and third study the King's Obesity Staging Criteria (KOSC) was used. The KOSC comprise nine health domains: Airways, Body mass index, Cardiovascular disease, Diabetes, Economic complications, Functional limitations, Gonadal axis, Health status (perceived), and Body Image. These domains were used to assess the effectiveness of Liraglutide 3.0mg for weight-loss (in the second study) and bariatric surgery for weight loss (in the third study). Results: In the first study on 5 patients, the following parameters were tested 1) apnea, 2) oxygenation, 3) symptoms of sleepiness, 4) sleep and 5) surrogate markers of cardiovascular risk after one-year bariatric surgery. We observed a significant improvement in the majority of polysomnography parameters. The severity classifications Apnea Hypopnea Index (AHI) used were mild (AHI = 5-14), moderate (AHI = 15- 30), and severe (AHI > 30). We found that participants' total AHI had improved significantly in Hypopnea (p < 0.03) and Apnea Hypopnea Index (p < 0.03). In the second study on 40 patients and the third study on 24 patients, the King's Obesity Staging Criteria (KOSC) comprise nine health domains: each domain, a person's health is assigned a score of 0 ('normal health'), 1 ('at risk'), 2('established disease'), or 3 ('advanced disease'). The two groups were analyzed using multivariate logistic regression to evaluate the null hypothesis that there is no change in participants' 9 domains between the two groups when measured at baseline and 52 weeks after commencement as part of the STRIVE RCT. Multivariate logistic regression showed a trend towards those without OSA benefiting more in cardiovascular health (p = 0.06). Those with OSA benefited more regarding improved in body image after 52 weeks (p =0.03). Conclusion: The three most important findings and the meaning of our study were improvements in the patient's OSA as measured by AHI. The study also demonstrates statistically significant correlations between AHI and anthropometric variables of weight, BMI, waist circumference, and neck circumference while suggesting baseline AHI may have been used as a predictor for AHI improvement with weight-loss 7 interventions. All oxygen parameters improved significantly after bariatric surgery and decreased the risk of coronary artery disease, heart attacks, heart failure, and strokes.


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The phenotype of T2D is rapidly evolving due to increasingly sedentary lifestyles and unhealthy eating behaviours. Many individuals are diagnosed with T2D at a younger age. This presents unique challenges including early onset of complications and impaired physical function. Experts agree that the overall aim of T2D management should be to maintain optimal glycaemic control and avoid complications while achieving good quality of life and functional independence with due consideration of social determinants of health. Consequently, there is an emerging need to encourage use of “newer” glucose-lowering therapies (GLTs), which confer both glycaemic and metabolic benefits, in diverse populations.The focus of my thesis is to explore the use of “newer” or novel agents; namely sodium–glucose cotransporter 2 inhibitors (SGLT2i), dipeptidyl peptidase-4 inhibitors (DPP4is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs), in diverse T2D populations which have not been focus of much research. These include;• young-onset T2D• people with impaired physical function• people who fast during Ramadan• people who had COVID-19 infectionThe use of novel GLTs is expected to improve metabolic and functional outcomes for these populations.The initial plan of my PhD was to focus on the impact of dapagliflozin, an SGLT2i, on physical function in people with T2D, through a randomised-controlled trial (RCT). However, this was adversely impacted by the COVID-19 pandemic. Recruitment was delayed by ~12 months and when it began, targets were difficult to achieve due to staffing issues and social distancing restrictions. Thus, it was agreed to undertake additional projects focusing on use of novel therapies in novel populations. Undertaking this additional work meant acquiring new skills, expanding knowledge and networking with external peers. As a result, this thesis represents large body of diverse but interconnected work which has been either published or presented at conferences.I have highlighted my contributions in individual chapters.


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/articles/thesis/Use_of_novel_glucose-lowering_therapies_in_novel_populations_with_type_2_diabetes/27043756/1


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Pharmacologic Treatment of Obesity in adults and its impact on comorbidities: 2024 Update and Position Statement of Specialists from the Brazilian Association for the Study of Obesity and Metabolic Syndrome (Abeso) and the Brazilian Society of Endocrinology and Metabolism (SBEM)


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Abstract page for arXiv paper 1411.5360v1: Communication over the network of binary switches regulates the activation of A$_{2A}$ adenosine receptor


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Communication over the network of binary switches regulates the activation of A$_{2A}$ adenosine receptor


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Dynamics and functions of G-protein coupled receptors (GPCRs) are accurately regulated by the type of ligands that bind to the orthosteric or allosteric binding sites. To glean the structural and dynamical origin of ligand-dependent modulation of GPCR activity, we performed total $\sim$ 5 $μ$sec molecular dynamics simulations of A$_{2A}$ adenosine receptor (A$_{2A}$AR) in its apo, antagonist-bound, and agonist-bound forms in an explicit water and membrane environment, and examined the corresponding dynamics and correlation between the 10 key structural motifs that serve as the allosteric hotspots in intramolecular signaling network. We dubbed these 10 structural motifs "binary switches" as they display molecular interactions that switch between two distinct states. By projecting the receptor dynamics on these binary switches that yield $2^{10}$ microstates, we show that (i) the receptors in apo, antagonist-bound, and agonist-bound states explore vastly different conformational space; (ii) among the three receptor states the apo state explores the broadest range of microstates; (iii) in the presence of the agonist, the active conformation is maintained through coherent couplings among the binary switches; and (iv) to be most specific, our analysis shows that W246, located deep inside the binding cleft, can serve as both an agonist sensor and actuator of ensuing intramolecular signaling for the receptor activation.Finally, our analysis of multiple trajectories generated by inserting an agonist to the apo state underscores that the transition of the receptor from inactive to active form requires the disruption of ionic-lock in the DRY motif.


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Abstract page for arXiv paper 2412.17780v3: PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion


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Peptide therapeutics, a major class of medicines, have achieved remarkable success across diseases such as diabetes and cancer, with landmark examples such as GLP-1 receptor agonists revolutionizing the treatment of type-2 diabetes and obesity. Despite their success, designing peptides that satisfy multiple conflicting objectives, such as target binding affinity, solubility, and membrane permeability, remains a major challenge. Classical drug development and structure-based design are ineffective for such tasks, as they fail to optimize global functional properties critical for therapeutic efficacy. Existing generative frameworks are largely limited to continuous spaces, unconditioned outputs, or single-objective guidance, making them unsuitable for discrete sequence optimization across multiple properties. To address this, we present PepTune, a multi-objective discrete diffusion model for the simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with state-dependent masking schedules and penalty-based objectives. To guide the diffusion process, we propose a Monte Carlo Tree Search (MCTS)-based strategy that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity inherent to discrete spaces. Using PepTune, we generate diverse, chemically-modified peptides optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling characteristics on various disease-relevant targets. In total, our results demonstrate that MCTS-guided discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces.


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Abstract page for arXiv paper 0809.1717v1: Molecular determinants involved in the allosteric control of agonist affinity in the GABAB receptor by the GABAB2 subunit


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Molecular determinants involved in the allosteric control of agonist affinity in the GABAB receptor by the GABAB2 subunit


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The gamma-aminobutyric acid type B (GABAB) receptor is an allosteric complex made of two subunits, GABAB1 (GB1) and GABAB2 (GB2). Both subunits are composed of an extracellular Venus flytrap domain (VFT) and a heptahelical domain (HD). GB1 binds GABA, and GB2 plays a major role in G-protein activation as well as in the high agonist affinity state of GB1. How agonist affinity in GB1 is regulated in the receptor remains unknown. Here, we demonstrate that GB2 VFT is a major molecular determinant involved in this control. We show that isolated versions of GB1 and GB2 VFTs in the absence of the HD and C-terminal tail can form hetero-oligomers as shown by time-resolved fluorescence resonance energy transfer (based on HTRF technology). GB2 VFT and its association with GB1 VFT controlled agonist affinity in GB1 in two ways. First, GB2 VFT exerted a direct action on GB1 VFT, as it slightly increased agonist affinity in isolated GB1 VFT. Second and most importantly, GB2 VFT prevented inhibitory interaction between the two main domains (VFT and HD) of GB1. According to this model, we propose that GB1 HD prevents the possible natural closure of GB1 VFT. In contrast, GB2 VFT facilitates this closure. Finally, such inhibitory contacts between HD and VFT in GB1 could be similar to those important to maintain the inactive state of the receptor.


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Abstract page for arXiv paper 2411.17967v1: QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative Data Extraction from Online Health Discussions


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QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative Data Extraction from Online Health Discussions


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Health-related discussions on social media like Reddit offer valuable insights, but extracting quantitative data from unstructured text is challenging. In this work, we present an adapted framework from QuaLLM into QuaLLM-Health for extracting clinically relevant quantitative data from Reddit discussions about glucagon-like peptide-1 (GLP-1) receptor agonists using large language models (LLMs). We collected 410k posts and comments from five GLP-1-related communities using the Reddit API in July 2024. After filtering for cancer-related discussions, 2,059 unique entries remained. We developed annotation guidelines to manually extract variables such as cancer survivorship, family cancer history, cancer types mentioned, risk perceptions, and discussions with physicians. Two domain-experts independently annotated a random sample of 100 entries to create a gold-standard dataset. We then employed iterative prompt engineering with OpenAI's "GPT-4o-mini" on the gold-standard dataset to build an optimized pipeline that allowed us to extract variables from the large dataset. The optimized LLM achieved accuracies above 0.85 for all variables, with precision, recall and F1 score macro averaged > 0.90, indicating balanced performance. Stability testing showed a 95% match rate across runs, confirming consistency. Applying the framework to the full dataset enabled efficient extraction of variables necessary for downstream analysis, costing under $3 and completing in approximately one hour. QuaLLM-Health demonstrates that LLMs can effectively and efficiently extract clinically relevant quantitative data from unstructured social media content. Incorporating human expertise and iterative prompt refinement ensures accuracy and reliability. This methodology can be adapted for large-scale analysis of patient-generated data across various health domains, facilitating valuable insights for healthcare research.


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Abstract page for arXiv paper 2112.07890v1: Investigating myocardial infarction and its effects in patients with urgent medical problems using advanced data mining tools


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Investigating myocardial infarction and its effects in patients with urgent medical problems using advanced data mining tools


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In medical science, it is very important to gather multiple data on different diseases and one of the most important objectives of the data is to investigate the diseases. Myocardial infarction is a serious risk factor in mortality and in previous studies, the main emphasis has been on people with heart disease and measuring the likelihood of myocardial infarction in them through demographic features, echocardiography, and electrocardiogram. In contrast, the purpose of the present study is to utilize data analysis algorithms and compare their accuracy in patients with a heart attack in order to identify the heart muscle strength during myocardial infarction by taking into account emergency operations and consequently predict myocardial infarction. For this purpose, 105 medical records of myocardial infarction patients with fourteen features including age, the time of emergency operation, Creatine Phosphokinase (CPK) test, heart rate, blood sugar, and vein are gathered and investigated through classification techniques of data analysis including random decision forests, decision tree, support vector machine (SVM), k-nearest neighbor, and ordinal logistic regression. Finally, the model of random decision forests with an accuracy of 76% is selected as the best model in terms of the mean evaluation indicator. Also, seven features of the creatine Phosphokinase test, urea, white and red blood cell count, blood sugar, time, and hemoglobin are identified as the most effective features of the ejection fraction variable.


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Abstract page for arXiv paper 1808.08375v1: Molecular dynamics simulations reveal the role of ceramicine B as novel PPARγ partial agonist against type 2 diabetes


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Molecular dynamics simulations reveal the role of ceramicine B as novel PPARγ partial agonist against type 2 diabetes


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Peroxisome proliferator-activated receptors gamma (PPARγ) are ligand-activated controllers of various metabolic actions and insulin sensitivity. PPARγ is thus considered as an important target to treat type 2 diabetes. Available PPARγ drugs (full agonists) have robust insulin-sensitizing properties but are accompanied by severe side effects leading to complicated health problems. Here, we have used molecular docking and a molecular dynamics simulation study to find a novel PPARγ ligand from a natural product. Our study suggests that the inhibition of ceramicine B in the PPARγ ligand-binding domain (LBD) could act as a partial agonist and block cdk5-mediated phosphorylation. This result may provide an opportunity for the development of new anti-diabetic drugs by targeting PPARγ while avoiding the side effects associated with full agonists.


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Abstract page for arXiv paper 2005.01887v2: Pain relief devoid of opioid side effects following central action of a silylated neurotensin analog


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Pain relief devoid of opioid side effects following central action of a silylated neurotensin analog


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Neurotensin (NT) exerts naloxone-insensitive antinociceptive action through its binding to both NTS1 and NTS2 receptors and NT analogs provide stronger pain relief than morphine on a molecular basis. Here, we examined the analgesic/adverse effect profile of a new NT(8-13) derivative denoted JMV2009, in which the Pro10 residue was substituted by a silicon-containing unnatural amino acid silaproline. We first report the synthesis and in vitro characterization (receptor-binding affinity, functional activity and stability) of JMV2009. We next examined its analgesic activity in a battery of acute, tonic and chronic pain models. We finally evaluated its ability to induce adverse effects associated with chronic opioid use, such as constipation and analgesic tolerance or related to NTS1 activation, like hypothermia. In in vitro assays, JMV2009 exhibited high binding affinity for both NTS1 and NTS2, improved proteolytic resistance as well as agonistic activities similar to NT, inducing sustained activation of p42/p44 MAPK and receptor internalization. Intrathecal injection of JMV2009 produced dose-dependent antinociceptive responses in the tail-flick test and almost completely abolished the nociceptive-related behaviors induced by chemical somatic and visceral noxious stimuli. Likewise, increasing doses of JMV2009 significantly reduced tactile allodynia and weight bearing deficits in nerve-injured rats. Importantly, chronic agonist treatment did not result in the development of analgesic tolerance. Furthermore, JMV2009 did not cause constipation and was ineffective in inducing hypothermia. These findings suggest that NT drugs can act as an effective opioid-free medication for the management of pain or can serve as adjuvant analgesics to reduce the opioid adverse effects.


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Abstract page for arXiv paper 2310.03235v2: Applying the causal roadmap to longitudinal national Danish registry data: a case study of second-line diabetes medication and dementia


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Applying the causal roadmap to longitudinal national Danish registry data: a case study of second-line diabetes medication and dementia


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The causal roadmap is a formal framework for causal and statistical inference that supports clear specification of the causal question, interpretable and transparent statement of required causal assumptions, robust inference, and optimal precision. The roadmap is thus particularly well-suited to evaluating longitudinal causal effects using large scale registries; however, application of the roadmap to registry data also introduces particular challenges. In this paper we provide a detailed case study of the longitudinal causal roadmap applied to the Danish National Registry to evaluate the comparative effectiveness of second-line diabetes drugs on dementia risk. Specifically, we evaluate the difference in counterfactual five-year cumulative risk of dementia if a target population of adults with type 2 diabetes had initiated and remained on GLP-1 receptor agonists (a second-line diabetes drug) compared to a range of active comparator protocols. Time-dependent confounding is accounted for through use of the iterated conditional expectation representation of the longitudinal g-formula as a statistical estimand. Statistical estimation uses longitudinal targeted maximum likelihood, incorporating machine learning. We provide practical guidance on the implementation of the roadmap using registry data, and highlight how rare exposures and outcomes over long-term follow up can raise challenges for flexible and robust estimators, even in the context of the large sample sizes provided by the registry. We demonstrate how simulations can be used to help address these challenges by supporting careful estimator pre-specification. We find a protective effect of GLP-1RAs compared to some but not all other second-line treatments.


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Obesity is a complex disease characterized by excessive fat accumulation which is caused by genetic, environmental and other factors. In recent years, there has been an increase in the morbidity, disability rate,and mortality due to obesity, making ...


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Abstract page for arXiv paper 1503.07441v1: Neuroreceptor Activation by Vibration-Assisted Tunneling


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G protein-coupled receptors (GPCRs) constitute a large family of receptor proteins that sense molecular signals on the exterior of a cell and activate signal transduction pathways within the cell. Modeling how an agonist activates such a receptor is fundamental for an understanding of a wide variety of physiological processes and it is of tremendous value for pharmacology and drug design. Inelastic electron tunneling spectroscopy (IETS) has been proposed as a model for the mechanism by which olfactory GPCRs are activated by a bound agonist. We apply this hypothesis to GPCRs within the mammalian nervous system using quantum chemical modeling. We found that non-endogenous agonists of the serotonin receptor share a particular IET spectral aspect both amongst each other and with the serotonin molecule: a peak whose intensity scales with the known agonist potencies. We propose an experiential validation of this model by utilizing lysergic acid dimethylamide (DAM-57), an ergot derivative, and its deuterated isotopologues; we also provide theoretical predictions for comparison to experiment. If validated our theory may provide new avenues for guided drug design and elevate methods of in silico potency/activity prediction.


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Tirzepatide (TZP) is a new anti-obesity drug, and little is currently known about its effect on body composition (BC) in people with overweight or obesity. The aim of this study is to conduct a systematic review on the impact of TZP on BC compartments in this population during weight loss programs. Literature searches, study selection, method development, and quality appraisal were performed. The data were synthesized using a narrative approach, in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Of the 1379 papers retrieved, 6 randomized controlled trials met the inclusion criteria and were reviewed, revealing the following findings. Firstly, TZP was shown to result in a significant reduction in total fat mass (FM), visceral adipose tissue (VAT) and waist circumference (WC) between baseline and short as well as intermediate follow-ups. Compared to other anti-obesity medications (e.g., dulaglutide and semaglutide) taken over the same duration, TZP showed a superior decrease in body fat compartments (i.e., total FM, VAT and WC). Finally, the effect of TZP on fat-free mass (FFM) is still uncertain because the findings remain inconclusive. In conclusion, TZP appears to be an effective strategy for achieving significant improvements in body fat and its distribution, but additional investigations are still needed to determine the impact of TZP on lean mass in this population.


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https://pub.mdpi-res.com/diseases/diseases-12-00204/article_deploy/html/images/diseases-12-00204-g001-550.jpg?1725595591


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The worldwide health emergency of obesity is closely connected to how dietary fats are metabolized, whereas the process is significantly influenced by pancreatic lipase (PL), an enzyme critical for lipid hydrolysis into fatty acids. This narrative review employs a methodological approach utilizing literature searches of PubMed data up to March 2024. The search term criteria encompasses keywords related to the role, mechanism, challenges, and current and future treatments of pancreatic lipase in obesity with an overall references is 106. This paper offers a comprehensive explanation of the role of PL, underlining its significance in the digestive process and lipid imbalances that contribute to obesity and by extension, its impact on obesity development and progression. Additionally, it delves into the dual functionality of the pancreas, emphasizing its impact on metabolism and energy utilization which, when dysregulated, promotes obesity. A focal point of this review is the investigation into the efficacy, challenges, and adverse effects of current pancreatic lipase inhibitors, with orlistat being highlighted as a primary current drug delivery. By discussing advanced obesity treatments, including the exploration of novel anti-obesity medications that target specific biological pathways, this review underscores the complexity of obesity treatment and the necessity for a multifaceted approach. In conclusion, this paper emphasizing the importance of understanding the role of enzymes like pancreatic lipase mechanistic and adopting a multidisciplinary approach to treatment and side effects of current obesity drugs and explore new emerging therapeutic strategies for more effective obesity management.


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Role of pancreatic lipase inhibition in obesity treatment: mechanisms and challenges towards current insights and future directions - International Journal of Obesity


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https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41366-025-01729-1/MediaObjects/41366_2025_1729_Figa_HTML.png


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In recent years, glucagon-like peptide 1 receptor agonists (GLP-1RAs) have become central in the treatment of type 2 diabetes (T2D). In addition to their glucose-lowering properties with low risk of hypoglycaemia, GLP-1RAs reduce body weight and show promising results in reducing cardiovascular risk and renal complications in high-risk individuals with T2D. These findings have changed guidelines on T2D management over the last years, and GLP-1RAs are now widely used in overweight patients with T2D as well as in patients with T2D and cardiovascular disease regardless of glycaemic control. The currently available GLP-1RAs have different pharmacokinetic profiles and differ in their ability to improve glycaemia, reduce body weight and in their cardio- and renal protective potentials. Understanding how these agents work, including insights into their pleiotropic effects on T2D pathophysiology, may improve their clinical utilisation and be useful for exploring other indications such as non-alcoholic steatohepatitis and neurodegenerative disorders. In this review, we provide an overview of approved GLP-1RAs, their clinical effects and mode of action, and we offer insights into the potential of GLP-1RAs for other indications than T2D. Finally, we will discuss the emerging data and therapeutic potential of using GLP-1RAs in combinations with other receptor agonists.


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Abstract page for arXiv paper q-bio/0401039v1: Regulation of Neuromodulator Receptor Efficacy - Implications for Whole-Neuron and Synaptic Plasticity


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Regulation of Neuromodulator Receptor Efficacy - Implications for Whole-Neuron and Synaptic Plasticity


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Membrane receptors for neuromodulators (NM) are highly regulated in their distribution and efficacy - a phenomenon which influences the individual cell's response to central signals of NM release. Even though NM receptor regulation is implicated in the pharmacological action of many drugs, and is also known to be influenced by various environmental factors, its functional consequences and modes of action are not well understood. In this paper we summarize relevant experimental evidence on NM receptor regulation (specifically dopamine D1 and D2 receptors) in order to explore its significance for neural and synaptic plasticity. We identify the relevant components of NM receptor regulation (receptor phosphorylation, receptor trafficking and sensitization of second-messenger pathways) gained from studies on cultured cells. Key principles in the regulation and control of short-term plasticity (sensitization) are identified, and a model is presented which employs direct and indirect feedback regulation of receptor efficacy. We also discuss long-term plasticity which involves shifts in receptor sensitivity and loss of responsivity to NM signals. Finally, we discuss the implications of NM receptor regulation for models of brain plasticity and memorization. We emphasize that a realistic model of brain plasticity will have to go beyond Hebbian models of long-term potentiation and depression. Plasticity in the distribution and efficacy of NM receptors may provide another important source of functional plasticity with implications for learning and memory.


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Abstract page for arXiv paper 2308.11936v2: General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold


key: og_title
value:
General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold


key: og_description
value:
This work presents a general chemical reaction network theory for olfactory sensing processes that employ G-protein-coupled receptors as olfactory receptors (ORs). The theory is applicable to general mixtures of odorants and an arbitrary number of ORs. Reactions of ORs with G-proteins, both in the presence and the absence of odorants, are explicitly considered. A unique feature of the theory is the definition of an odor activity vector consisting of strengths of odorant-induced signals from ORs relative to those due to background G-protein activity in the absence of odorants. It is demonstrated that each component of the odor activity defined this way reduces to a Michaelis-Menten form capable of accounting for cooperation or competition effects between different odorants. The main features of the theory are illustrated for a two-odorant mixture. Known and potential mixture effects, such as suppression, shadowing, inhibition, and synergy are quantitatively described. Effects of relative values of rate constants, basal activity, and G-protein concentration are also demonstrated.


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Abstract page for arXiv paper 1407.4750v1: Ligand-dependent opening of the multiple AMPA receptor conductance states: a concerted model


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Modulation of the properties of AMPA receptors at the post-synaptic membrane is one of the main suggested mechanisms behind synaptic plasticity in the central nervous system of vertebrates. Electrophysiological recordings of single channels stimulated with agonists showed that both recombinant and native AMPA receptors visit multiple conductance states in an agonist concentration dependent manner. We propose an allosteric model of the multiple conductance states based on concerted conformational transitions of the four subunits, as an iris diaphragm. Our model predicts that the thermodynamic behaviour of the conductance states upon full and partial agonist stimulations can be described with increased affinity of receptors as they progress to higher conductance states. The model also predicts existence of AMPA receptors in non-liganded conductive substates. However, spontaneous openings probability decreases with increasing conductances. Finally, we predict that the large conductance states are stabilized within the rise phase of a whole-cell EPSC in glutamatergic hippocampal neurons. Our model provides a mechanistic link between ligand concentration and conductance states that can explain thermodynamic and kinetic features of AMPA receptor gating.


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Abstract page for arXiv paper 2307.02463v1: An Euler-Bernoulli-Type Beam Model of the Vocal Folds for Describing Curved and Incomplete Glottal Closure Patterns


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An Euler-Bernoulli-Type Beam Model of the Vocal Folds for Describing Curved and Incomplete Glottal Closure Patterns


key: og_description
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Incomplete glottal closure is a laryngeal configuration wherein the glottis is not fully obstructed prior to phonation. In this work, we introduce an Euler-Bernoulli composite beam vocal fold (VF) model that produces qualitatively similar incomplete glottal closure patterns as those observed in experimental and high-fidelity numerical studies, thus offering insights in to the potential underlying physical mechanisms. Refined physiological insights are pursued by incorporating the beam model into a VF posturing model that embeds the five intrinsic laryngeal muscles. Analysis of the combined model shows that co-activating the lateral cricoarytenoid (LCA) and interarytenoid (IA) muscles without activating the thyroarytenoid (TA) muscle results in a bowed (convex) VF geometry with closure at the posterior margin only; this is primarily attributed to the reactive moments at the anterior VF margin. This bowed pattern can also arise during VF compression (due to extrinsic laryngeal muscle activation for example), wherein the internal moment induced passively by the TA muscle tissue is the predominant mechanism. On the other hand, activating the TA muscle without incorporating other adductory muscles results in anterior and mid-membranous glottal closure, a concave VF geometry, and a posterior glottal opening driven by internal moments induced by TA muscle activation. In the case of initial full glottal closure, the posterior cricoarytenoid (PCA) muscle activation cancels the adductory effects of the LCA and IA muscles, resulting in a concave VF geometry and posterior glottal opening. Furthermore, certain maneuvers involving co-activation of all adductory muscles result in an hourglass glottal shape due to a reactive moment at the anterior VF margin and moderate internal moment induced by TA muscle activation.


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Abstract page for arXiv paper 2406.16433v1: Multiscale Mechanical Modeling of Skeletal Muscle: A Systemic Review of the Literature


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Purpose: From the myofibrils to the whole muscle scale, muscle micro-constituents exhibit passive and active mechanical properties, potentially coupled to electrical, chemical, and thermal properties. Experimental characterization of some of these properties is currently not available for all muscle constituents. Multiscale multiphysics models have recently gained interest as a numerical alternative to investigate the healthy and diseased physiological behavior of the skeletal muscle. Methods: This paper refers to the multiscale mechanical models proposed in the literature to investigate the mechanical properties and behavior of skeletal muscles. More specifically, we focus on the scale transition methods, constitutive laws and experimental data implemented in these models. Results: Using scale transition methods such as homogenization, coupled to appropriate constitutive behavior of the constituents, these models explore the mechanisms of ageing, myopathies, sportive injuries, and muscle contraction. Conclusion: Emerging trends include the development of multiphysics simulations and the coupling of modeling with the acquisition of experimental data at different scales, with increasing focus to little known constituents such as the extracellular matrix and the protein titin.


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Obesity is becoming more frequent and has several negative health impacts. Recent advances in weight management strategies have primarily resided in pharmaceutical treatments, and the glucagon-like peptide-1 (GLP-1) receptor agonists have shown ...


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BackgroundGlucagon-like peptide-1 receptor-agonists (GLP-1ra), such as semaglutide, have emerged as promising treatments, demonstrating sustained weight redu...


key: keywords
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Semaglutide-Induced Body Composition Transformation in Adults with Obesity and Type 2 Diabetes Mellitus Obesity,type 2 diabetes mellitus,Body Composition,phase angle,fat mass,Lean Body Mass,visceral fat area,HbA1c


key: og_image
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https://images-provider.frontiersin.org/api/ipx/w=1200&f=png/https://www.frontiersin.org/files/Articles/1386542/fendo-15-1386542-HTML/image_m/fendo-15-1386542-g001.jpg


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Discover how creatine interacts with weight loss medications, its effects, benefits, and potential risks to optimize your fitness journey.


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Boost your weight loss success with GLP-1 medications by incorporating weightlifting, creatine supplementation, and a high-protein diet. Learn how these strategies preserve muscle, enhance metabolism, and improve long-term results.


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Abstract page for arXiv paper 1711.00928v1: Physiological Tremor Increases when Skeletal Muscle is Shortened: Implications for Fusimotor Control


key: og_title
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Physiological Tremor Increases when Skeletal Muscle is Shortened: Implications for Fusimotor Control


key: og_description
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The involuntary force fluctuations associated with physiological (as distinct from pathological) tremor are an unavoidable component of human motor control. While the origins of the physiological tremor are known to depend on muscle afferentation, it is possible that the mechanical properties of muscle-tendon systems also affect its generation, amplification and maintenance. In this paper, we investigated the dependence of physiological tremor during tonic, isometric plantarflextion torque at 30% of maximum at three ankle angles. The amplitude of physiological tremor increased as calf muscles shortened in contrast to the stretch reflex whose amplitude decreases as muscle shortens. We used a closed-loop simulation model of afferented muscle to explore the mechanisms responsible for this behavior. We demonstrate that changing muscle lengths does not suffice to explain our experimental findings. Rather, the model consistently required the modulation of gamme-static fusimotor drive to produce increases in physiological tremor with muscle shortening--while successfully replicating the concomitant reduction in stretch reflex amplitude. This need to control gamma-static fusimotor drive explicitly as a function of muscle length has important implication. First, it permits the amplitudes of physiological tremor and stretch reflex to be decoupled. Second, it postulates neuromechanical interaction that require length-dependent gamma drive modulation to be independent from alpha drive to the parent muscle. Lastly, it suggests that physiological tremor can be used as a simple, non-invasive measure of the afferent mechanisms underlying healthy motor function, and their disruption in neurological conditions.


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Diabetic kidney disease (DKD) is a substantial complication of type 2 diabetes (T2D), presenting challenges in chronic kidney disease (CKD) management. In addition to traditional and recent therapies, including angiotensin, converting enzyme (ACE) inhibitors, angiotensin receptor blockers, sodium-glucose cotransporter 2 (SGLT2) inhibitors, and mineralocorticoid receptor antagonists, the evolution of antihyperglycemic treatments has introduced a promising agent, glucagon-like peptide-1 receptor agonist (GLP-1RA) for the management of DKD. GLP-1RAs enhance insulin release and reduce glucagon release, offering a novel approach to DKD management. This review analyzes the molecular pathways through which GLP1-RAs confer renal protection in T2D and DKD, which are complex and multifaceted. They include modulation of renal hemodynamics, antioxidative and anti-inflammatory actions, metabolic regulation, and direct cellular effects. These mechanisms highlight GLP1-RA’s potential as a therapeutic option for glycemic control and direct or indirect renal function protection in diabetic patients, emphasizing the potentiality of GLP-1RAs for dual therapy, with cardiovascular and renal protection as a holistic approach. Clinical evidence supports GLP-1RAs in reducing albuminuria and enhancing kidney outcomes, highlighting their value in a comprehensive DKD management strategy.


key: keywords
value:
pmid:39761578, doi:10.7326/ANNALS-24-01590, Systematic Review, Review, Areesha Moiz, Kristian B Filion, Mark J Eisenberg, Adult, Anti-Obesity Agents / adverse effects, Anti-Obesity Agents / therapeutic use, Drug Therapy, Combination, Glucagon-Like Peptide-1 Receptor Agonists, Glucagon-Like Peptide-1 Receptor*, Glucagon-Like Peptides* / adverse effects, Glucagon-Like Peptides* / analogs & derivatives, Glucagon-Like Peptides* / therapeutic use, Humans, Hypoglycemic Agents / adverse effects, Hypoglycemic Agents / therapeutic use, Liraglutide* / adverse effects, Liraglutide* / therapeutic use, Obesity* / complications, Obesity* / drug therapy, Overweight / complications, Overweight / drug therapy, Randomized Controlled Trials as Topic*, Weight Loss* / drug effects, PubMed Abstract, NIH, NLM, NCBI, National Institutes of Health, National Center for Biotechnology Information, National Library of Medicine, MEDLINE


key: description
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Glucagon-like peptide-1 (GLP-1) agonists are a class of medications utilized to treat type 2 diabetes mellitus (T2DM) and obesity. As a class of medications, they are among several pharmacological options for these endocrine diseases. The function of GLP-1 agonists is to lower serum glucose levels and thereby manage metabolism in affected patients. Participating clinicians gain an enhanced understanding of when to prescribe these agonists, consider specific patient populations, and seek consultations with specialists in the care of their patients. Recent recommendations regarding mixed formulations are also included in alignment with current research on this drug class. This activity will highlight the indications, mechanism of action, administration, adverse effect profile, and contraindications for these drugs. An interprofessional team of nurses, primary care clinicians, pharmacists, and endocrinologists remains pertinent to care for patients prescribed this class of medications.


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The treatment of type 2 diabetes (T2D) necessitates a multifaceted approach that combines behavioral and pharmacological interventions to mitigate complications and sustain a high quality of life. Treatment encompasses the management of glucose levels, weight, cardiovascular risk factors, comorbidities, and associated complications through medication and lifestyle adjustments. Metformin, a standard in diabetes management, continues to serve as the primary, first-line oral treatment across all age groups due to its efficacy, versatility in combination therapy, and cost-effectiveness. Glucagon-like peptide-1 receptor agonists (GLP-1 RA) offer notable benefits for HbA1c and weight reduction, with significant cardiovascular benefits. Sodium-glucose cotransporter inhibitors (SGLT-2i) lower glucose levels independently of insulin while conferring notable benefits for cardiovascular, renal, and heart-failure outcomes. Combined therapies emphasizing early and sustained glycemic control are promising options for diabetes management. As insulin therapy remains pivotal, metformin and non-insulin agents such as GLP-1 RA and SGLT-2i offer compelling options. Notably, exciting novel treatments like the dual GLP-1/ glucose-dependent insulinotropic polypeptide (GIP) agonist show promise for substantially reducing glycated hemoglobin and body weight. This comprehensive review highlights the evolving landscape of pharmacotherapy in diabetes, the drugs currently available for treating diabetes, their effectiveness and efficacy, the impact on target organs, and side effects. This work also provides insights that can support the customization of treatment strategies.


key: og_image
value:
https://pub.mdpi-res.com/ijms/ijms-24-17147/article_deploy/html/images/ijms-24-17147-g001-550.jpg?1701774197


key: description
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Obesity is a multifactorial chronic disease characterized by an excess of adipose tissue, affecting people of all ages. In the last 40 years, the incidence o...


key: keywords
value:
Obesity,visceral adiposity,Hypertension,type 2 diabetes mellitus,adipocentric,GLP-1 receptor agonists,SGLT-2 inhibitors,Cardiometabolic complications


key: og_image
value:
https://images-provider.frontiersin.org/api/ipx/w=1200&f=png/https://www.frontiersin.org/files/Articles/1365183/fmed-11-1365183-HTML/image_m/fmed-11-1365183-g001.jpg


key: description
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While lifestyle changes such as dietary modification and increased physical activity can be very effective in improving glycemic control, over the long-term most individuals with Type 2 diabetes (T2DM) will require medications to achieve and maintain glycemic control. The purpose of this chapter is to provide the healthcare practitioner with an overview of the existing oral and injectable (non-insulin) pharmacological options available for the treatment of patients with T2DM. Currently, there are ten classes of orally available pharmacological agents to treat T2DM: 1) sulfonylureas, 2) meglitinides, 3) metformin (a biguanide), 4) thiazolidinediones (TZDs), 5) alpha glucosidase inhibitors, 6) dipeptidyl peptidase IV (DPP-4) inhibitors, 7) bile acid sequestrants, 8) dopamine agonists, 9) sodium-glucose transport protein 2 (SGLT2) inhibitors and 10) oral glucagon like peptide 1 (GLP-1) receptor agonists. In addition, glucagon like peptide 1 (GLP-1) receptor agonists, dual GLP-1 receptor and GIP receptor agonists, and pramlintide can be administered by injection. Medications from these distinct classes of pharmaceutical agents may be used as treatment by themselves (monotherapy) or in a combination of 2 or more drugs from multiple classes with different mechanisms of action. A variety of fixed combinations of 2 agents are available in the US and in many other countries. In this chapter we discuss the administration, mechanism of action, effect on glycemic control, other benefits, side effects, and the contraindications of the use of these glucose lowering drugs. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG.


key: author
value:
Evangelos Oikonomou|Maria Xenou|George E. Zakynthinos|Paraskevas Tsaplaris|Stamatios Lampsas|Evanthia Bletsa|Ioannis Gialamas|Konstantinos Kalogeras|Athina Goliopoulou|Maria I. Gounaridi|Theodoros Pesiridis|Aikaterini Tsatsaragkou|Manolis Vavouranakis|Gerasimos Siasos|Dimitris Tousoulis


key: description
value:
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in individuals with diabetes mellitus (DM). Although benefit has been attributed to the strict control of hyperglycemia with traditional antidiabetic treatments, novel antidiabetic medications have demonstrated
cardiovascular (CV) safety and benefits by reducing major adverse cardiac events, improving heart failure (HF), and decreasing CVD-related mortality. Emerging data underline the interrelation between diabetes, as a metabolic disorder, and inflammation, endothelial dysfunction, and oxidative
stress in the pathogenesis of microvascular and macrovascular complications. Conventional glucose-lowering medications demonstrate controversial CV effects. Dipeptidyl peptidase- 4 inhibitors have not only failed to prove to be beneficial in patients with coronary artery disease, but also
their safety is questionable for the treatment of patients with CVD. However, metformin, as the first-line option for type 2 DM (T2DM), shows CVD protective properties for DM-induced atherosclerotic and macrovascular complications. Thiazolidinedione and sulfonylureas have questionable effects,
as evidence from large studies shows a reduction in the risk of CV events and deaths, but with an increased rate of hospitalization for HF. Moreover, several studies have revealed that insulin monotherapy for T2DM treatment increases the risk of major CV events and deaths from HF, when compared
to metformin, although it may reduce the risk of myocardial infarction. Finally, this review aimed to summarize the mechanisms of action of novel antidiabetic drugs acting as glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter-2 inhibitors that show favorable effects
on blood pressure, lipid levels, and inflammation, leading to reduced CVD risk in T2DM patients.


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Abstract page for arXiv paper 2209.03448v1: Designing an Optimized Electric Vehicle Charging Station Infrastructure for Urban Area: A Case study from Indonesia


key: og_title
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Designing an Optimized Electric Vehicle Charging Station Infrastructure for Urban Area: A Case study from Indonesia


key: og_description
value:
The rapid development of electric vehicle (EV) technologies promises cleaner air and more efficient transportation systems, especially for polluted and congested urban areas. To capitalize on this potential, the Indonesian government has appointed PLN, its largest state-owned electricity provider, to accelerate the preparation of Indonesia's EV infrastructure. With a mission of providing reliable, accessible, and cost-effective EV charging station infrastructure throughout the country, the company is prototyping a location-optimized model to simulate how well its infrastructure design reaches customers, fulfills demands, and generates revenue. In this work, we study how PLN could maximize profit by optimally placing EV charging stations in urban areas by adopting a maximal covering location model. In our experiments, we use data from Surabaya, Indonesia, and consider the two main transportation modes for the locals to charge: electric motorcycles and electric cars. Numerical experiments show that only four charging stations are needed to cover the whole city, given the charging technology that PLN has acquired. However, consumers' time-to-travel is exceptionally high (about 35 minutes), which could lead to poor consumer service and hindrance toward EV technologies. Sensitivity analysis reveals that building more charging stations could reduce the time but comes with higher costs due to extra facility installations. Adding layers of redundancy to buffer against outages or other disruptions also incurs higher costs but could be an appealing option to design a more reliable and thriving EV infrastructure. The model can provide insights to decision-makers to devise the most reliable and cost-effective infrastructure designs to support the deployment of electric vehicles and much more advanced intelligent transportation systems in the near future.


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Life-cycle assessment of the greenhouse gas emissions of passenger cars in China, Europe, India, and the United States, which together account for about 70% of new passenger car sales worldwide.


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Abstract page for arXiv paper 2403.16830v1: Exploring Communication Technologies, Standards, and Challenges in Electrified Vehicle Charging


key: og_description
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As public awareness of environmental protection continues to grow, the trend of integrating more electric vehicles (EVs) into the transportation sector is rising. Unlike conventional internal combustion engine (ICE) vehicles, EVs can minimize carbon emissions and potentially achieve autonomous driving. However, several obstacles hinder the widespread adoption of EVs, such as their constrained driving range and the extended time required for charging. One alternative solution to address these challenges is implementing dynamic wireless power transfer (DWPT), charging EVs in motion on the road. Moreover, charging stations with static wireless power transfer (SWPT) infrastructure can replace existing gas stations, enabling users to charge EVs in parking lots or at home. This paper surveys the communication infrastructure for static and dynamic wireless charging in electric vehicles. It encompasses all communication aspects involved in the wireless charging process. The architecture and communication requirements for static and dynamic wireless charging are presented separately. Additionally, a comprehensive comparison of existing communication standards is provided. The communication with the grid is also explored in detail. The survey gives attention to security and privacy issues arising during communications. In summary, the paper addresses the challenges and outlines upcoming trends in communication for EV wireless charging.


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Abstract page for arXiv paper 1612.07568v2: Pedestrian-Aware Engine Management Strategies for Plug-in Hybrid Electric Vehicles


key: og_description
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Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. While hybrid vehicles are usually designed with the objective of minimising fuel consumption, in this paper we propose engine management strategies that also take into account environmental effects of the vehicles to pedestrians outside of the vehicles. Specifically, we present optimisation based engine energy management strategies for PHEVs, that attempt to minimise the environmental impact of pedestrians along the route of the vehicle, while taking account of route dependent uncertainties. We implement the proposed approach in a real PHEV, and evaluate the performance in a hardware-in-the-loop platform. A variety of simulation results are given to illustrate the efficacy of our proposed approach.


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Abstract page for arXiv paper 2502.09779v1: Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis


key: og_title
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Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis


key: og_description
value:
Body composition assessment using CT images can potentially be used for a number of clinical applications, including the prognostication of cardiovascular outcomes, evaluation of metabolic health, monitoring of disease progression, assessment of nutritional status, prediction of treatment response in oncology, and risk stratification for surgical and critical care outcomes. While multiple groups have developed in-house segmentation tools for this analysis, there are very limited publicly available tools that could be consistently used across different applications. To mitigate this gap, we present a publicly accessible, end-to-end segmentation and feature calculation model specifically for CT body composition analysis. Our model performs segmentation of skeletal muscle, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) across the chest, abdomen, and pelvis area in axial CT images. It also provides various body composition metrics, including muscle density, visceral-to-subcutaneous fat (VAT/SAT) ratio, muscle area/volume, and skeletal muscle index (SMI), supporting both 2D and 3D assessments. The model is shared for public use. To evaluate the model, the segmentation was applied to both internal and external datasets, with body composition metrics analyzed across different age, sex, and race groups. The model achieved high dice coefficients on both internal and external datasets, exceeding 89% for skeletal muscle, SAT, and VAT segmentation. The model outperforms the benchmark by 2.40% on skeletal muscle and 10.26% on SAT compared to the manual annotations given by the publicly available dataset. Body composition metrics show mean relative absolute errors (MRAEs) under 10% for all measures. Furthermore, the model provided muscular fat segmentation with a Dice coefficient of 56.27%, which can be utilized for additional analyses as needed.


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New obesity medications result in large weight losses. However, long-term adherence in a real-world setting is challenging, and termination of obesity medication results in weight regain towards pre-treatment body weight. Therefore, we investigated ...


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How does weight loss differ between patients receiving tirzepatide compared with semaglutide among a clinical population of adults with overweight or obesity? In this cohort study of 18 386 propensity-score matched patients initiating tirzepatide or ...


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Abstract page for arXiv paper 2502.17484v1: Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity


key: og_title
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Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity


key: og_description
value:
Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD), as they can lead to severe complications if not detected and treated early. This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD by analysing in-home activity and physiological data collected through low-cost, passive sensors. The current research focuses on improving the performance of previous models, particularly by refining the Multilayer Perceptron (MLP), to better handle variations in home environments and improve sex fairness in predictions by making use of concepts from multitask learning. This study implemented three primary model designs: feature clustering, loss-dependent clustering, and participant ID embedding which were compared against a baseline MLP model. The results demonstrated that the loss-dependent MLP achieved the most significant improvements, increasing validation precision from 48.92% to 72.60% and sensitivity from 27.44% to 70.52%, while also enhancing model fairness across sexes. These findings suggest that the refined models offer a more reliable and equitable approach to early UTI detection in PLWD, addressing participant-specific data variations and enabling clinicians to detect and screen for UTI risks more effectively, thereby facilitating earlier and more accurate treatment decisions.


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Abstract page for arXiv paper 2309.02604v1: Screening of Pneumonia and Urinary Tract Infection at Triage using TriNet


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Due to the steady rise in population demographics and longevity, emergency department visits are increasing across North America. As more patients visit the emergency department, traditional clinical workflows become overloaded and inefficient, leading to prolonged wait-times and reduced healthcare quality. One of such workflows is the triage medical directive, impeded by limited human workload, inaccurate diagnoses and invasive over-testing. To address this issue, we propose TriNet: a machine learning model for medical directives that automates first-line screening at triage for conditions requiring downstream testing for diagnosis confirmation. To verify screening potential, TriNet was trained on hospital triage data and achieved high positive predictive values in detecting pneumonia (0.86) and urinary tract infection (0.93). These models outperform current clinical benchmarks, indicating that machine-learning medical directives can offer cost-free, non-invasive screening with high specificity for common conditions, reducing the risk of over-testing while increasing emergency department efficiency.


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Abstract page for arXiv paper 1708.01601v1: A Clinical and Finite Elements Study of Stress Urinary Incontinence in Women Using Fluid-Structure Interactions


key: og_title
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A Clinical and Finite Elements Study of Stress Urinary Incontinence in Women Using Fluid-Structure Interactions


key: og_description
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Stress Urinary Incontinence (SUI) or urine leakage from urethra occurs due to an increase in abdominal pressure resulting from stress like a cough or jumping height. SUI is more frequent among post-menopausal women. In the absence of bladder contraction, vesical pressure exceeds from urethral pressure leading to urine leakage. Despite a large number of patients diagnosed with this problem, few studies have investigated its function and mechanics. The main goal of this study is to model bladder and urethra computationally under an external pressure like sneezing. Finite Element Method and Fluid-Structure Interactions are utilized for simulation. Linear mechanical properties assigned to the bladder and urethra and pressure boundary conditions are indispensable in this model. The results show good accordance between the clinical data and predicted values of the computational models, such as the pressure at the center of the bladder. This indicates that numerical methods and simplified physics of biological systems like inferior urinary tract are helpful to achieve the results similar to clinical results, in order to investigate pathological conditions.


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Abstract page for arXiv paper 2111.09414v1: Developmental Status and Perspectives for Tissue Engineering in Urology


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Tissue engineering technology and tissue cell-based stem cell research have made great strides in treating tissue and organ damage, correcting tissue and organ dysfunction, and reducing surgical complications. In the past, traditional methods have used biological substitutes for tissue repair materials, while tissue engineering technology has focused on merging sperm cells with biological materials to form biological tissues with the same structure and function as their own tissues. The advantage is that tissue engineering technology can overcome donors. Material procurement restrictions can effectively reduce complications. The aim of studying tissue engineering technology is to find sperm cells and suitable biological materials to replace the original biological functions of tissues and to establish a suitable in vivo microenvironment. This article mainly describes the current developments of tissue engineering in various fields of urology and discusses the future trends of tissue engineering technology in the treatment of complex diseases of the urinary system. The results of the research in this article indicate that while the current clinical studies are relatively few, the good results from existing animal model studies indicate good prospects of tissue engineering technology for the treatment of various urinary tract diseases in the future.


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Abstract page for arXiv paper 2312.11355v1: Vesicoureteral Reflux Detection with Reliable Probabilistic Outputs


key: og_description
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Vesicoureteral Reflux (VUR) is a pediatric disorder in which urine flows backwards from the bladder to the upper urinary tract. Its detection is of great importance as it increases the risk of a Urinary Tract Infection, which can then lead to a kidney infection since bacteria may have direct access to the kidneys. Unfortunately the detection of VUR requires a rather painful medical examination, called voiding cysteourethrogram (VCUG), that exposes the child to radiation. In an effort to avoid the exposure to radiation required by VCUG some recent studies examined the use of machine learning techniques for the detection of VUR based on data that can be obtained without exposing the child to radiation. This work takes one step further by proposing an approach that provides lower and upper bounds for the conditional probability of a given child having VUR. The important property of these bounds is that they are guaranteed (up to statistical fluctuations) to contain well-calibrated probabilities with the only requirement that observations are independent and identically distributed (i.i.d.). Therefore they are much more informative and reliable than the plain yes/no answers provided by other techniques.


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Abstract page for arXiv paper 1501.03537v2: Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection


key: og_title
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Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection


key: og_description
value:
Lower urinary tract symptoms (LUTS) can indicate the presence of urinary tract infection (UTI), a condition that if it becomes chronic requires expensive and time consuming care as well as leading to reduced quality of life. Detecting the presence and gravity of an infection from the earliest symptoms is then highly valuable. Typically, white blood cell count (WBC) measured in a sample of urine is used to assess UTI. We consider clinical data from 1341 patients at their first visit in which UTI (i.e. WBC$\geq 1$) is diagnosed. In addition, for each patient, a clinical profile of 34 symptoms was recorded. In this paper we propose a Bayesian nonparametric regression model based on the Dirichlet Process (DP) prior aimed at providing the clinicians with a meaningful clustering of the patients based on both the WBC (response variable) and possible patterns within the symptoms profiles (covariates). This is achieved by assuming a probability model for the symptoms as well as for the response variable. To identify the symptoms most associated to UTI, we specify a spike and slab base measure for the regression coefficients: this induces dependence of symptoms selection on cluster assignment. Posterior inference is performed through Markov Chain Monte Carlo methods.


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Abstract page for arXiv paper 2402.09498v1: Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques


key: og_title
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Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques


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Background: Postpartum urinary incontinence (PUI) is a common issue among postnatal women. Previous studies identified potential related variables, but lacked analysis on certain intrinsic and extrinsic patient variables during pregnancy.
  Objective: The study aims to evaluate the most influential variables in PUI using machine learning, focusing on intrinsic, extrinsic, and combined variable groups.
  Methods: Data from 93 pregnant women were analyzed using machine learning and oversampling techniques. Four key variables were predicted: occurrence, frequency, intensity of urinary incontinence, and stress urinary incontinence.
  Results: Models using extrinsic variables were most accurate, with 70% accuracy for urinary incontinence, 77% for frequency, 71% for intensity, and 93% for stress urinary incontinence.
  Conclusions: The study highlights extrinsic variables as significant predictors of PUI issues. This suggests that PUI prevention might be achievable through healthy habits during pregnancy, although further research is needed for confirmation.


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Tadalafil, a phosphodiesterase type 5 (PDE5) inhibitor, is approved worldwide for the treatment of lower urinary tract symptoms secondary to benign prostatic hyperplasia (BPH-LUTS). The purpose of this narrative review is to summarize the clinical ...


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Yunuo Wu, PharmD Community Practice Resident Creighton University School of Pharmacy and Health Professions Michael H. Davidian, MD, MS Associate Professor of Medicine Creighton University School of Medicine Edward M. DeSimone II, RPh, PhD, FAPhA Professor of Pharmacy Sciences Creighton University School of Pharmacy and Health Professions Omaha, Nebraska


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Can using Cialis for BPH be an effective treatment to relieve benign prostatic hyperplasia? Learn from our prostate experts as they take a closer look.


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Benign prostatic hyperplasia (BPH) is the most common cause of lower urinary tract symptom (LUTS) development in men [1]. The intensity of the symptoms may vary from mild to severe, significantly affecting the quality of life. Erectile dysfunction ...


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The goal of this revised guideline is to provide a useful reference on the effective evidence-based surgical management of male lower urinary tract symptoms secondary to benign prostatic hyperplasia (LUTS/BPH).


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benign prostatic hyperplasia, male, lower urinary tract symptoms, urology, guidelines, guidance, treatment, diagnosis, BPH


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Lower Urinary Tract Symptoms (LUTS) in men are a common clinical problem in urology and have been historically strictly linked to benign prostatic hyperplasia (BPH), which may lead to bladder outlet obstruction (BOO). New molecules have been approved and have entered the urologists’ armamentarium, targeting new signaling pathways and tackling specific aspects of LUTS. Objective of this review is to summarize the evidence regarding the new medical therapies currently available for male non-neurogenic LUTS, including superselective α1-antagonists, PDE-5 inhibitors, anticholinergic drugs and intraprostatic onabotulinum toxin injections. The National Library of Medicine Database was searched for relevant articles published between January 2006 and December 2015, including the combination of “BPH”, “LUTS”, “medical” and “new”. Each article’s title, abstract and text were reviewed for their appropriateness and their relevance. One hundred forty eight articles were reviewed. Of the 148 articles reviewed, 92 were excluded. Silodosin may be considered a valid alternative to non-selective α1-antagonists, especially in the older patients where blood pressure alterations may determine major clinical problems and ejaculatory alterations may be not truly bothersome. Tadalafil 5&nbsp;mg causes a significant decrease of IPSS score with an amelioration of patients’ QoL, although with no significant increase in Qmax. Antimuscarinic drugs are effective on storage symptoms but should be used with caution in patients with elevated post-void residual. Intraprostatic injections of botulinum toxin are well-tolerated and effective, with a low rate of adverse events; however profound ameliorations were seen also in the sham arms of RCTs evaluating intraprostatic injections. New drugs have been approved in the last years in the medical treatment of BPH-related LUTS. Practicing urologists should be familair with their pharmacodynamics and pharmacokinetics.


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New medical treatments for lower urinary tract symptoms due to benign prostatic hyperplasia and future perspectives - BMC Urology


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Background Lower Urinary Tract Symptoms (LUTS) in men are a common clinical problem in urology and have been historically strictly linked to benign prostatic hyperplasia (BPH), which may lead to bladder outlet obstruction (BOO). New molecules have been approved and have entered the urologists’ armamentarium, targeting new signaling pathways and tackling specific aspects of LUTS. Objective of this review is to summarize the evidence regarding the new medical therapies currently available for male non-neurogenic LUTS, including superselective α1-antagonists, PDE-5 inhibitors, anticholinergic drugs and intraprostatic onabotulinum toxin injections. Methods The National Library of Medicine Database was searched for relevant articles published between January 2006 and December 2015, including the combination of “BPH”, “LUTS”, “medical” and “new”. Each article’s title, abstract and text were reviewed for their appropriateness and their relevance. One hundred forty eight articles were reviewed. Results Of the 148 articles reviewed, 92 were excluded. Silodosin may be considered a valid alternative to non-selective α1-antagonists, especially in the older patients where blood pressure alterations may determine major clinical problems and ejaculatory alterations may be not truly bothersome. Tadalafil 5 mg causes a significant decrease of IPSS score with an amelioration of patients’ QoL, although with no significant increase in Qmax. Antimuscarinic drugs are effective on storage symptoms but should be used with caution in patients with elevated post-void residual. Intraprostatic injections of botulinum toxin are well-tolerated and effective, with a low rate of adverse events; however profound ameliorations were seen also in the sham arms of RCTs evaluating intraprostatic injections. Conclusion New drugs have been approved in the last years in the medical treatment of BPH-related LUTS. Practicing urologists should be familair with their pharmacodynamics and pharmacokinetics.


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https://static-content.springer.com/image/art%3A10.1186%2Fs12894-016-0176-0/MediaObjects/12894_2016_176_Fig1_HTML.gif


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Benign prostatic hyperplasia (BPH) is the most common benign disease of the prostate gland and is caused by benign hyperplasia of the smooth muscle cells and...


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https://images-provider.frontiersin.org/api/ipx/w=1200&f=png/https://www.frontiersin.org/files/Articles/528249/fphar-11-00658-HTML/image_m/fphar-11-00658-g001.jpg


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Background: Tadalafil has been approved for the treatment of benign prostatic hyperplasia (BPH) for nearly 10 years. However, there are insufficient evidence...


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https://images-provider.frontiersin.org/api/ipx/w=1200&f=png/https://www.frontiersin.org/files/Articles/744012/fmed-08-744012-HTML/image_m/fmed-08-744012-g001.jpg


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The relationship between erectile dysfunction (ED) and lower urinary tract symptoms (LUTS) caused by benign prostatic obstruction (BPO) has recently gained


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Guide to Drug Therapy for Lower Urinary Tract Symptoms in Patients with Benign Prostatic Obstruction - Drugs


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The relationship between erectile dysfunction (ED) and lower urinary tract symptoms (LUTS) caused by benign prostatic obstruction (BPO) has recently gained increasing attention. Both BPO and ED are highly prevalent in older men and both conditions frequently contribute to a reduction in overall quality of life.Current medical treatment of LUTS/BPO consists of monotherapy with α1-adrenoceptor antagonists or 5α-reductase inhibitors, a combination of these two agents or, in some cases, various phytotherapeutic approaches. When choosing a drug therapy, it is important to recognize that while 5α-reductase inhibitors increase the risk of ED and ejaculatory disorders, and combined therapy carries the cumulative risk of causing sexual dysfunction, some α1-adrenergic receptor antagonists have been reported to improve overall sexual function. Therefore, the successful evaluation and management of older men with LUTS associated with BPO should include an assessment of baseline sexual function and subsequent monitoring of medication-induced sexual adverse effects.In this review, we detail the pathophysiological mechanisms involved in LUTS/BPO-associated ED, including reduced nitric oxide/cyclic guanosine monophosphate system activity, enhanced endothelin-1/rhoA/rho kinase pathway activity, sympathetic overactivity, pelvic organ atherosclerosis and potential preventive approaches.


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https://static-content.springer.com/image/art%3A10.2165%2F00003495-200868020-00005/MediaObjects/40265_2012_68020209_Fig1.jpg


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Urinary Tract Symptoms (LUTS) Secondary to Benign Prostatic Hyperplasia (BPH) and LUTS/BPH with Erectile Dysfunction in Asian Men: A Systematic Review Focusing on Tadalafil


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Park HJ, et al. World J Mens Health. 2013 Dec;31(3):193-207. https://doi.org/10.5534/wjmh.2013.31.3.193


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We study a bilateral trade problem where a principal has private information that is revealed with delay, such as a seller who does not yet know her production cost. Postponing the contracting process incurs a costly delay, while early contracting with limited information can create incentive issues, as the principal might misrepresent private information that will be revealed later. We show that the optimal mechanism can effectively address these challenges by leveraging the sequential nature of the problem. The optimal mechanism is a menu of two-part tariffs, where the variable part is determined by the principal's incentives and the fixed part by the agent's incentives. As two-part tariffs might be impractical in some applications, we also study price mechanisms. We show that the optimal price mechanism often entails trade at both the ex-ante and ex-post stages. Dynamic price mechanisms can lower the cost of delay by transacting with high-type agents early and relax the incentive constraints by postponing contracts with lower-type agents. We also generalize our analysis to costly learning and study ex-post efficiency in our context.


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Abstract page for arXiv paper 1806.04238v1: Estimating Trade-Related Adjustment Costs in the Agricultural Sector in Iran


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Tariff liberalization and its impact on tax revenue is an important consideration for developing countries, because they are increasingly facing the difficult task of implementing and harmonizing regional and international trade commitments. The tariff reform and its costs for Iranian government is one of the issues that are examined in this study. Another goal of this paper is, estimating the cost of trade liberalization. On this regard, imports value of agricultural sector in Iran in 2010 was analyzed according to two scenarios. For reforming nuisance tariff, a VAT policy is used in both scenarios. In this study, TRIST method is used. In the first scenario, imports' value decreased to a level equal to the second scenario and higher tariff revenue will be created. The results show that reducing the average tariff rate does not always result in the loss of tariff revenue. This paper is a witness that different forms of tariff can generate different amount of income when they have same level of liberalization and equal effect on producers. Therefore, using a good tariff regime can help a government to generate income when increases social welfare by liberalization.


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Tariffs have been applied over the years to protect homegrown industries and target competitors who are seen as using unfair trade practices. They impose costs on both importers and exporters and had been in decline until the recent U.S.-China trade war.


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President Donald Trump has begun his second term with a raft of tariff threats to bolster his immigration priorities, support local industries, and compete with China. Here’s how these taxes work and…


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Abstract page for arXiv paper 1702.01796v1: On the Efficiency of Connection Charges---Part II: Integration of Distributed Energy Resources


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This two-part paper addresses the design of retail electricity tariffs for distribution systems with distributed energy resources (DERs). Part I presents a framework to optimize an ex-ante two-part tariff for a regulated monopolistic retailer who faces stochastic wholesale prices on the one hand and stochastic demand on the other. In Part II, the integration of DERs is addressed by analyzing their endogenous effect on the optimal two-part tariff and the induced welfare gains. Two DER integration models are considered: (i) a decentralized model involving behind-the-meter DERs in a net metering setting, and (ii) a centralized model involving DERs integrated by the retailer. It is shown that DERs integrated under either model can achieve the same social welfare and the net-metering tariff structure is optimal. The retail prices under both integration models are equal and reflect the expected wholesale prices. The connection charges differ and are affected by the retailer's fixed costs as well as the statistical dependencies between wholesale prices and behind-the-meter DERs. In particular, the connection charge of the decentralized model is generally higher than that of the centralized model. An empirical analysis is presented to estimate the impact of DER on welfare distribution and inter-class cross-subsidies using real price and demand data and simulations. The analysis shows that, with the prevailing retail pricing and net-metering, consumer welfare decreases with the level of DER integration. Issues of cross-subsidy and practical drawbacks of decentralized integration are also discussed.


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Abstract page for arXiv paper 2203.12173v2: The Impact of Geopolitical Conflicts on Trade, Growth, and Innovation


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Geopolitical conflicts have increasingly been a driver of trade policy. We study the potential effects of global and persistent geopolitical conflicts on trade, technological innovation, and economic growth. In conventional trade models the welfare costs of such conflicts are modest. We build a multi-sector multi-region general equilibrium model with dynamic sector-specific knowledge diffusion, which magnifies welfare losses of trade conflicts. Idea diffusion is mediated by the input-output structure of production, such that both sector cost shares and import trade shares characterize the source distribution of ideas. Using this framework, we explore the potential impact of a "decoupling of the global economy," a hypothetical scenario under which technology systems would diverge in the global economy. We divide the global economy into two geopolitical blocs -- East and West -- based on foreign policy similarity and model decoupling through an increase in iceberg trade costs (full decoupling) or tariffs (tariff decoupling). Results yield three main insights. First, the projected welfare losses for the global economy of a decoupling scenario can be drastic, as large as 15% in some regions and are largest in the lower income regions as they would benefit less from technology spillovers from richer areas. Second, the described size and pattern of welfare effects are specific to the model with diffusion of ideas. Without diffusion of ideas the size and variation across regions of the welfare losses would be substantially smaller. Third, a multi-sector framework exacerbates diffusion inefficiencies induced by trade costs relative to a single-sector one.


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We analyze competition on nonlinear prices in homogeneous goods markets with consumer search. In equilibrium firms offer two-part tariffs consisting of a linear price and lump-sum fee. The equilibrium production is socially efficient as the linear price of equilibrium two-part tariffs equals to the production marginal cost. Firms thus compete in lump-sum fees, which are dispersed in equilibrium. We show that sellers enjoy higher profit, whereas consumers are worse-off with two-part tariffs than with linear prices. The competition softens because with two-part tariffs firms can make effective per-consumer demand less elastic than the actual demand.


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Abstract page for arXiv paper 1610.08415v1: A Comparison of Various Electricity Tariff Price Forecasting Techniques in Turkey and Identifying the Impact of Time Series Periods


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A Comparison of Various Electricity Tariff Price Forecasting Techniques in Turkey and Identifying the Impact of Time Series Periods


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It is very vital for suppliers and distributors to predict the deregulated electricity prices for creating their bidding strategies in the competitive market area. Pre requirement of succeeding in this field, accurate and suitable electricity tariff price forecasting tools are needed. In the presence of effective forecasting tools, taking the decisions of production, merchandising, maintenance and investment with the aim of maximizing the profits and benefits can be successively and effectively done. According to the electricity demand, there are four various electricity tariffs pricing in Turkey; monochromic, day, peak and night. The objective is find the best suitable tool for predicting the four pricing periods of electricity and produce short term forecasts (one year ahead-monthly). Our approach based on finding the best model, which ensures the smallest forecasting error measurements of: MAPE, MAD and MSD. We conduct a comparison of various forecasting approaches in total accounts for nine teen, at least all of those have different aspects of methodology. Our beginning step was doing forecasts for the year 2015. We validated and analyzed the performance of our best model and made comparisons to see how well the historical values of 2015 and forecasted data for that specific period matched. Results show that given the time-series data, the recommended models provided good forecasts. Second part of practice, we also include the year 2015, and compute all the models with the time series of January 2011 to December 2015. Again by choosing the best appropriate forecasting model, we conducted the forecast process and also analyze the impact of enhancing of time series periods (January 2007 to December 2015) to model that we used for forecasting process.


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Abstract page for arXiv paper 2403.12215v2: Aggregate Peak EV Charging Demand: The Impact of Segmented Network Tariffs


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Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asset users to shift their demand from peak periods. However, fewer studies quantify the effect of these tariff schemes on the aggregate level. In this paper, we compare the effect of a multi-level segmented network tariff with and without dynamic energy prices for individual EV users on the aggregate peak demand. Results based on real charging transactions from over 1200 public charging points in the Netherlands show that the segmented network tariff with flat energy prices results in more diverse load profiles with increasing aggregation, as compared to cost-optimized dispatch based on only dynamic day-ahead energy prices. When paired with dynamic energy prices, the segmented tariff still outperforms only dynamic energy price-based tariffs in reducing peaks. Results show that a balance between power thresholds and price per threshold is crucial in designing a suitable tariff, taking into account the needs of the power network. We also provide valuable insights to network operators by calculating the diversity factor for various peak demands per charging point.


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I use the unanticipated and large additional tariffs the US imposed on European Union products due to the Airbus-Boeing conflict to analyze how exporters reacted to a change in trade policy. Using firm-level data for Spain and applying a difference-in-differences methodology, I show that the export revenue in the US of the firms affected by the tariff hike did not significantly decrease relative to the one of other Spanish exporters to the US. I show that Spanish exporters were able to neutralize the increase in tariffs by substituting Spanish products with products originated in countries unaffected by tariffs and shifting to varieties not affected by tariffs. My results show that tariff avoidance is another margin exporters can use to counteract the effects of a tariff hike.


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What happens to pollution when developing countries open their borders to trade? Theoretical predictions are ambiguous, and empirical evidence remains limited. We study the effects of the 1991 Indian trade liberalization reform on water pollution. The reform abruptly and unexpectedly lowered import tariffs, increasing exposure to trade. Larger tariff reductions are associated with relative increases in water pollution. The estimated effects imply a 0.12 standard deviation increase in water pollution for the median district exposed to the tariff reform.


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Most products are produced and sold by supply chain networks, where an interconnected network of producers and intermediaries set prices to maximize their profits. I show that there exists a unique equilibrium in a price-setting game on a network. The key distortion reducing both total profits and social welfare is multiple-marginalization, which is magnified by strategic interactions. Individual profits are proportional to influentiality, which is a new measure of network centrality defined by the equilibrium characterization. The results emphasize the importance of the network structure when considering policy questions such as mergers or trade tariffs.


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Abstract page for arXiv paper 2408.13706v1: Import competition and domestic vertical integration: Theory and Evidence from Chinese firms


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What impact does import competition have on firms' production organizational choices? Existing literature has predominantly focused on the relationship between import competition and firms' global production networks, with less attention given to domestic. We first develop a Nash-bargaining model to guide our empirical analysis, then utilize tariff changes as an exogenous shock to test our theoretical hypotheses using a database of Chinese listed firms from 2000 to 2023. Our findings indicate that a decrease in downstream tariffs lead to an increase in vertical integration. In our mechanism tests, we discover that a reduction in upstream tariffs also enhances this effect. Moreover, the impact of tariff reductions on vertical integration is primarily observed in industries with high asset specificity, indicating that asset-specificity is a crucial mechanism. We further explore whether import competition encourages vertical integration for technological acquisition purpose, the effect is found only among high-tech firms, while it's absent in non-high-tech firms. Our research provides new perspectives and evidence on how firms optimize their production organization in the process of globalization.


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Abstract page for arXiv paper 2202.05517v1: Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs


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In electricity markets, retailers or brokers want to maximize profits by allocating tariff profiles to end consumers. One of the objectives of such demand response management is to incentivize the consumers to adjust their consumption so that the overall electricity procurement in the wholesale markets is minimized, e.g. it is desirable that consumers consume less during peak hours when cost of procurement for brokers from wholesale markets are high. We consider a greedy solution to maximize the overall profit for brokers by optimal tariff profile allocation. This in-turn requires forecasting electricity consumption for each user for all tariff profiles. This forecasting problem is challenging compared to standard forecasting problems due to following reasons: i. the number of possible combinations of hourly tariffs is high and retailers may not have considered all combinations in the past resulting in a biased set of tariff profiles tried in the past, ii. the profiles allocated in the past to each user is typically based on certain policy. These reasons violate the standard i.i.d. assumptions, as there is a need to evaluate new tariff profiles on existing customers and historical data is biased by the policies used in the past for tariff allocation. In this work, we consider several scenarios for forecasting and optimization under these conditions. We leverage the underlying structure of how consumers respond to variable tariff rates by comparing tariffs across hours and shifting loads, and propose suitable inductive biases in the design of deep neural network based architectures for forecasting under such scenarios. More specifically, we leverage attention mechanisms and permutation equivariant networks that allow desirable processing of tariff profiles to learn tariff representations that are insensitive to the biases in the data and still representative of the task.


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Tariffs on goods imported to the U.S. are not new, but today they are being discussed as much broader tools in a more complicated world.Since before the Constitution was signed, tariffs have reduced the competitiveness of American enterprise and increased the cost of goods. Today, they are proposed to bring back U.S. manufacturing jobs to communities that provided the president his strongest support while also being leveraged as expansive geopolitical tools with uncertain consequences.


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Photo of a large container ship, seen from the stern, and docked next to a large crane. Many other containers are stacked high on the dock, and the lower trusses of a green bridge can be seen spanning over the ship and port.


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A tariff is a tax levied by the government on imported goods. They raise costs for importers who usually pass them on to consumers in the form of higher priced goods.


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In this post, we estimate the effect of these tariffs on the prices paid by U.S. producers and consumers.


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https://libertystreeteconomics.newyorkfed.org/wp-content/uploads/sites/2/2019/01/6a01348793456c970c022ad3cb7747200b-500wi.jpg


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Learn about tariffs, their role in trade, and their effects on manufacturing. Explore both sides of the debate in this insightful blog post.


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The setting of public utility prices involves balancing various competing government policy objectives, from equity concerns to ensuring the financial sustainability of providers and balancing public finances. In practice, public utility pricing often departs significantly from government objectives and tends to be characterized by unnecessarily complex price schedules, below cost-recovery tariff rates, and sectoral inefficiencies that contribute to large fiscal costs. Countries commonly embark on utility pricing reform in response to these heavy fiscal pressures. The paper discusses various reform options available to governments, with a focus on residential pricing schedules, highlighting their fiscal, financial, redistributive, and efficiency implications.


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This paper explores the gain maximization problem of two nations engaging in non-cooperative bilateral trade. Probabilistic model of an exchange of commodities under different price systems is considered. Volume of commodities exchanged determines the demand each nation has over the counter party's currency. However, each nation can manipulate this quantity by imposing a tariff on imported commodities. As long as the gain from trade is determined by the balance between imported and exported commodities, such a scenario results in a two party game where Nash equilibrium tariffs are determined for various foreign currency demand functions and ultimately, the exchange rate based on optimal tariffs is obtained.


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Since 2018, there has been a consistent decline in the distance traveled by U.S. manufacturing imports, reaching a level not observed since 2008. This trend is the result of the substitution away from imports from China and towards imports from closer countries. At the same time, U.S. manufacturing inventory-to-sales ratio has continued to rise. These trends are at odds with the literature, which finds that reductions in the distance of imports are associated with a decline in inventories. We argue that a rise in delivery time risk, driven by longer and more frequent delays and supply disruptions, can reconcile these trends. We do so in the context of a model of global sourcing with stochastic delivery times and inventories. Firms trade off the lower price of farther inputs with the increase in exposure to demand volatility and longer delays. In response, firms increase their inventories. Yet, as delivery delays rise, firms need to carry more inventories per unit of the input used. We calibrate the model for the period from 2018 to 2024 using data on the increase in tariffs for inputs from China, and the rise in inventories over sales. We find an increase in delivery delays for foreign inputs of 21 days across the period. The rise in delays and tariffs had an output loss of 7.3% and a price increase of 1.8%. Of these, the rise in delivery delays alone generated a 2.6% drop in output and a 0.4% increase in prices.


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Abstract page for arXiv paper 2010.02378v3: Protectionism and economic growth: Causal evidence from the first era of globalization


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We investigate how protectionist policies influence economic growth. Our empirical strategy exploits an extraordinary tax scandal that gave rise to an unexpected change of government in Sweden. A free-trade majority in parliament was overturned by a protectionist majority in 1887. The protectionist government increased tariffs. We employ the synthetic control method to select control countries against which economic growth in Sweden can be compared. We do not find evidence suggesting that protectionist policies influenced economic growth and examine channels why. The new tariff laws increased government revenue. However, the results do not suggest that the protectionist government stimulated the economy by increasing government expenditure.


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Abstract page for arXiv paper 2409.12516v1: A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets -- A New Microfoundations of GARCH model


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A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets -- A New Microfoundations of GARCH model


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The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model to quantitatively assess the specific impacts of AI traders remains undeveloped. This study aims to address this gap by modeling the influence of AI traders on market price formation and volatility within a multi-agent framework, leveraging the concept of microfoundations. Microfoundations involve understanding macroeconomic phenomena, such as market price formation, through the decision-making and interactions of individual economic agents. While widely acknowledged in macroeconomics, microfoundational approaches remain unexplored in empirical finance, particularly for models like the GARCH model, which captures key financial statistical properties such as volatility clustering and fat tails. This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH model. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets. Finally, we analyze the impact of AI traders using parameters derived from these microfoundations, contributing to a deeper understanding of their role in market dynamics.


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Abstract page for arXiv paper 1612.08486v1: Understanding the Impacts of Dark Pools on Price Discovery


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This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signals about an asset's fundamental. We find that informed traders use dark pools to mitigate their information risk and there is a sorting effect: in equilibrium, traders with strong signals trade in exchanges, traders with moderate signals trade in dark pools, and traders with weak signals do not trade. As a result, dark pools have an amplification effect on price discovery. That is, when information precision is high (information risk is low), the majority of informed traders trade in the exchange hence adding a dark pool enhances price discovery, whereas when information precision is low (information risk is high), the majority of the informed traders trade in the dark pool hence adding a dark pool impairs price discovery. The paper reconciles the conflicting empirical evidence and produces novel empirical predictions. The paper also provides regulatory suggestions with dark pools on current equity markets and in emerging markets.


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Abstract page for arXiv paper 1802.09954v4: Price Impact Under Heterogeneous Beliefs and Restricted Participation


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We consider a financial market in which traders potentially face restrictions in trading some of the available securities. Traders are heterogeneous with respect to their beliefs and risk profiles, and the market is assumed thin: traders strategically trade against their price impacts. We prove existence and uniqueness of a corresponding equilibrium, and provide an efficient algorithm to numerically obtain the equilibrium prices and allocations given market's inputs. We find that restrictions may increase the market's welfare if traders have different views regarding the covariance matrix of securities returns. The latter heterogeneity regarding covariance matrix disagreement is essential in modelling; for instance, when traders agree on the covariance matrix, restricting participation in some securities for some traders leaves equilibrium prices unaltered in the unrestricted securities, a certainly undesirable model effect.


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Abstract page for arXiv paper 2412.20847v1: Strategic Learning and Trading in Broker-Mediated Markets


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We study strategic interactions in a broker-mediated market. A broker provides liquidity to an informed trader and to noise traders while managing inventory in the lit market. The broker and the informed trader maximise their trading performance while filtering each other's private information; the trader estimates the broker's trading activity in the lit market while the broker estimates the informed trader's private signal. Brokers hold a strategic advantage over traders who rely solely on prices to filter information. We find that information leakage in the client's trading flow yields an economic value to the broker that is comparable to transaction costs; she speculates profitably and mitigates risk effectively, which, in turn, adversely impacts the informed trader's performance. In contrast, low signal-to-noise sources, such as prices, result in the broker's trading performance being indistinguishable from that of a naive strategy that internalises noise flow, externalises informed flow, and offloads inventory at a constant rate.


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Abstract page for arXiv paper 1702.01792v1: On the Efficiency of Connection Charges---Part I: A Stochastic Framework


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This two-part paper addresses the design of retail electricity tariffs for distribution systems with distributed energy resources such as solar power and storage. In particular, the optimal design of dynamic two-part tariffs for a regulated monopolistic retailer is considered, where the retailer faces exogenous wholesale electricity prices and fixed costs on the one hand and stochastic demands with inter-temporal price dependencies on the other. Part I presents a general framework and analysis for revenue adequate retail tariffs with advanced notification, dynamic prices and uniform connection charges. It is shown that the optimal two-part tariff consists of a dynamic price that may not match the expected wholesale price and a connection charge that distributes uniformly among all customers the retailer's fixed costs and a price-volume risk premium. A sufficient condition for the optimality of the derived two-part tariff among the class of arbitrary ex-ante tariffs is obtained. Numerical simulations quantify the substantial welfare gains that the optimal two-part tariff may bring compared to the optimal linear tariff (without connection charge). Part II focuses on the impact of two-part tariffs on the integration of distributed energy resources.


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Experts are tracking the economic impact of the Trump trade war, including the initial Trump tariffs and proposed Trump tariffs. See more.


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The Trump administration imposed nearly $80 billion worth of new taxes on Americans by levying tariffs on thousands of products in 2018 and 2019, amounting to one of the largest tax increases in decades. The Biden administration has kept most of the Trump administration tariffs in place


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https://taxfoundation.org/wp-content/uploads/2024/06/Trump-tariffs-Trump-trade-war-tracking-economic-impact-tax-foundation-analysis.jpg


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Tariffs are taxes on international trade. Here's everything you need to know about trade barriers and tariffs, why they are used, and their effects on the local economy.


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https://www.investopedia.com/thmb/nLXu64uU1zCO3rHBn4kgghpwUcE=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/GettyImages-968819844-f084399962054d939aff07fd3946132e.jpg


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The Political Economy of the World Trading System is a comprehensive textbook account of the economics, institutional mechanics and politics of the world trading system. This third edition has been expanded and updated to cover developments in the World Trade Organisation (WTO) since its formation, including the Doha Round, presenting the essentials of trade negotiations and the WTO's rules and disciplines.  The authors focus in particular on the WTO's role as the primary organisation through which trading nations manage their commercial interactions and the focal point for cooperation on policy responses to the rapidly changing global trading environment. It is the forum in which many features of the globalisation process are considered, and it currently faces an unprecedented set of challenges. The increasing importance of countries in Asia, Latin America and Africa in international trade relations, the revealed preference towards regionalism, intensification of trade conflicts, the role of business groups and NGOs in trade policy formation and negotiations, and pressures for more leadership in an institution threatened by paralysis are examples of issues that are discussed in some detail; all are critical for the operation of the system and for international business in the coming decade. This edition also includes numerous real-world examples to illustrate how the WTO impinges on business, workers and households, written from the perspective of managers and business associations.  An insider's view of the institutional history of the WTO allows the authors to use a variety of conceptual tools to analyse the working of the WTO in a non-technical manner. Suggestions for Further Reading at the end of each chapter and an extensive bibliography make the volume suitable both for introductory and postgraduate courses on international economics and business, international relations, and international economic law.


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https://books.google.com/books/publisher/content?id=YWIVDAAAQBAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE73BAHJoPr4BrhY2YvS4xGGL2J1IPxSEGkF6rkwZRdmlN4BvXD_Ijgm0V6Empq4Tpv3jqTnaSWYvW_OfESmFJViQxN60KJgR5l_mxiXUvY7AdMQkdXqhpBU6rVep7sSeAbgQGfpS


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The emergence of globalization was neither accidental nor inevitable. To make the "free flow" of commodities, capital, and money possible, governments first had to introduce a new political infrastructure. In Remaking U.S. Trade Policy, Nitsan Chorev focuses on trade liberalization in the United States from the 1930s to the present as she explores the political origins of today's global economy.The ability of the U.S. government to impose its preferences on other governments is an important part of the story of globalization, but what is central to Chorev's analysis is understanding why the nation's leaders supported trade liberalization in the first place. For Chorev, the explanation lies in domestic political struggles. Advocates of free trade prevailed in the struggle with protectionists by working to change the institutions governing trade policy, replacing institutional arrangements that favored protectionism with new ones that favored a free-market approach.The new institutional arrangements shifted authority from a protectionist Congress to liberal agencies at the executive branch and to the World Trade Organization. These transformations entailed a move from a politicized location, in which direct negotiations and debates dominate the process of decision-making, to bureaucratic and judicial arenas where a legal logic dominates and the citizens have little voice.


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https://books.google.com/books/content?id=kEOFTNgPl3MC&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE70JWsGUWPYIJl2Czk5FHcoTWlcu4vhVJLPGnundZrVbp3iq6T7lo2E5XK-anjwxQyO3JkNWWhJJr4Ix9rU98Wp5RQ2k77yTqld0Ng9B39Dpldyluu2AwilO-M9jr5CPmFjac1uj


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Economists have had an enormous impact on trade policy, and they provide a strong rationale for free trade and for removal of trade barriers.  Although the objective of a trade agreement is to liberalize trade, the actual provisions are heavily shaped by domestic and international political realities. The world has changed enormously from the time when David Ricardo proposed the law of comparative advantage, and in recent decades economists have modified their theories to account for trade in factors of production, such as capital and labor, the growth of supply chains that today dominate much of world trade, and the success of neomercantilist countries in achieving rapid growth.


key: og_image
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https://books.google.com/books/content?id=eQ-xAAAAIAAJ&printsec=frontcover&img=1&zoom=1&edge=curl&imgtk=AFLRE71GFv7ur-EFXg5WP8M8wCzp61V4iMecZzNO6vKFEQyPHu1G9EOxmfpuaGvG0irxaRybV0oz54t1WxWpWFPQNXpuC4NO59pruy86oifNfIKohvuF-juiFgnuF_DrK8ar3yrNfufs


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One of the most salient changes in the world economy since 1980 has been the move toward freer trade among countries across the globe. How do existing theories


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Drawing on a wide variety of classic and contemporary sources, respected authors Howse and Trebilcock here provide a critical analysis of the institutions and


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https://www.taylorfrancis.com/books/mono/10.4324/9780203799796/regulation-international-trade-robert-howse-antonia-eliason-robert-howse-michael-trebilcock


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Smart tariffs are an important part of industrial policy, but Trump’s tariff agenda is ill-conceived, self-defeating, and harmful to workers.


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https://www.cambridge.org/core/journals/international-organization/article/abs/transformation-of-trade-the-sources-and-effects-of-regime-change/062EB4DC671332D6836D9E9A9545A8E1


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https://static.cambridge.org/covers/INO_0_0_0/international_organization.jpg?send-full-size-image=true


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https://www.cambridge.org/core/journals/international-organization/article/abs/why-the-move-to-free-trade-democracy-and-trade-policy-in-the-developing-countries/7B851DC230706EFDE2295D1DFA8ECC15


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Why the Move to Free Trade? Democracy and Trade Policy in the Developing Countries | International Organization | Cambridge Core


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Why the Move to Free Trade? Democracy and Trade Policy in the Developing Countries - Volume 59 Issue 1


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Abstract page for arXiv paper 2112.04218v1: Global Financial Cycle, Commodity Terms of Trade and Financial Spreads in Emerging Markets and Developing Economies


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Global Financial Cycle, Commodity Terms of Trade and Financial Spreads in Emerging Markets and Developing Economies


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We study the diffusion of shocks in the global financial cycle and global liquidity conditions to emerging and developing economies. We show that the classification according to their external trade patterns (as commodities' net exporters or net importers) allows to evaluate the relative importance of international monetary spillovers and their impact on the domestic financial cycle volatility -i.e., the coefficient of variation of financial spreads and risks. Given the relative importance of commodity trade in the economic structure of these countries, our study reveals that the sign and size of the trade balance of commodity goods are key parameters to rationalize the impact of global financial and liquidity conditions. Hence, the sign and volume of commodity external trade will define the effect on countries' financial spreads. We implement a two-equation dynamic panel data model for 33 countries during 1999:Q1-2020:Q4 that identifies the effect of global conditions on the countries' commodities terms of trade and financial spreads, first in a direct way, and then by a feedback mechanism by which the terms of trade have an asymmetric additional influence on spreads.


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Primer on the impact of tariffs and trade on the United States. Analysis of the growth of free trade and its benefits to the U.S. economy. U.S. trade impact


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Abstract page for arXiv paper 2306.07211v1: Differential Game Analysis for Cooperation Models in Automotive Supply Chain under Low-Carbon Emission Reduction Policies


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Differential Game Analysis for Cooperation Models in Automotive Supply Chain under Low-Carbon Emission Reduction Policies


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In the context of reducing carbon emissions in the automotive supply chain, collaboration between vehicle manufacturers and retailers has proven to be an effective measure for enhancing carbon emission reduction within the enterprise. This study aims to evaluate the effectiveness of such collaboration by constructing a differential game model that incorporates carbon trading and consumer preferences for low-carbon products. The model examines the decision-making process of an automotive supply chain comprising a vehicle manufacturer and multiple retailers. By utilizing the Hamilton-Jacobi-Bellman equation, we analyze the equilibrium strategies of the participants under both a decentralized model and a Stackelberg leader-follower game model. In the decentralized model, the vehicle manufacturer optimizes its carbon emission reduction efforts, while each retailer independently determines its low-carbon promotion efforts and vehicle retail price. In the Stackelberg leader-follower game model, the vehicle manufacturer cooperates with the retailers by offering them a subsidy. Consequently, the manufacturer plays as the leader, making decisions on carbon emission reduction efforts and the subsidy rate, while the retailers, as followers, compute their promotion efforts and retail prices accordingly. Through theoretical analysis and numerical experiments considering the manufacturer's and retailers' efforts, the low-carbon reputation of vehicles, and the overall system profits under both models, we conclude that compared to the decentralized model, where each party pursues individual profits, the collaboration in the Stackelberg game yields greater benefits for both parties. Furthermore, this collaborative approach promotes the long-term development of the automotive supply chain.


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The Hutchins Center explains what tariffs are, how they work, and what's happened with them during the Trump and Biden administrations.


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https://www.brookings.edu/wp-content/uploads/2024/07/aerial-view-of-shipping-containers-at-dock.jpg?quality=75


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Abstract page for arXiv paper 2309.02271v1: Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?


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Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?


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The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.


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Abstract page for arXiv paper 2012.08355v1: A mathematical model of national-level food system sustainability


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The global food system faces various endogeneous and exogeneous, biotic and abiotic risk factors, including a rising human population, higher population densities, price volatility and climate change. Quantitative models play an important role in understanding food systems' expected responses to shocks and stresses. Here, we present a stylised mathematical model of a national-level food system that incorporates domestic supply of a food commodity, international trade, consumer demand, and food commodity price. We derive a critical compound parameter signalling when domestic supply will become unsustainable and the food system entirely dependent on imports, which results in higher commodity prices, lower consumer demand and lower inventory levels. Using Bayesian estimation, we apply the dynamic food systems model to infer the sustainability of the UK pork industry. We find that the UK pork industry is currently sustainable but because the industry is dependent on imports to meet demand, a decrease in self-sufficiency below 50% (current levels are 60-65%) would lead it close to the critical boundary signalling its collapse. Our model provides a theoretical foundation for future work to determine more complex causal drivers of food system vulnerability.


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Abstract page for arXiv paper 2201.04944v1: Peer-to-Peer Energy Trading in a Microgrid Leveraged by Smart Contracts


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The current electricity networks were not initially designed for the high integration of variable generation technologies. They suffer significant losses due to the combustion of fossil fuels, the long-distance transmission, and distribution of the power to the network. Recently, \emph{prosumers}, both consumers and producers, emerge with the increasing affordability to invest in domestic solar systems. Prosumers may trade within their communities to better manage their demand and supply as well as providing social and economic benefits. In this paper, we explore the use of Blockchain technologies and auction mechanisms to facilitate autonomous peer-to-peer energy trading within microgrids. We design two frameworks that utilize the smart contract functionality in Ethereum and employ the continuous double auction and uniform-price double-sided auction mechanisms, respectively. We validate our design by conducting A/B tests to compare the performance of different frameworks on a real-world dataset. The key characteristics of the two frameworks and several cost analyses are presented for comparison. Our results demonstrate that a P2P trading platform that integrates the blockchain technologies and agent-based systems is promising to complement the current centralized energy grid. We also identify a number of limitations, alternative solutions, and directions for future work.


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During his presidential campaign, President Trump pledged to impose universal tariffs of 10–60% on all U.S. imports—a whopping $4.2 trillion in goods and services purchased from abroad&nbsp;in 2024. This was always a real possibility. The International Economic Emergency Powers Act gives the president broad authority to do so. In early February the Trump administration seemed…


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February 4, 2025 Negotiations between leaders in Mexico, Canada and the U.S. on Monday put a 30-day pause on President Donald Trump’s promised tariffs that would increase U.S. taxes on goods from Mexico and Canada each by 25%. Rodney Sullivan spoke with UVA Today to explain more about tariffs and how they may affect Americans. –


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Negotiations between leaders in Mexico, Canada and the U.S. on Monday put a 30-day pause on President Donald Trump’s promised tariffs that would increase U.S. taxes on goods from Mexico and Canada each by 25%. Rodney Sullivan spoke with UVA Today to explain more about tariffs and how they may affect Americans.


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Global trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations. Traditionally, economists model trade using gravity models, which rely on explicit covariates but often struggle to capture these subtler drivers of trade. In this work, we employ optimal transport and a deep neural network to learn a time-dependent cost function from data, without imposing a specific functional form. This approach consistently outperforms traditional gravity models in accuracy while providing natural uncertainty quantification. Applying our framework to global food and agricultural trade, we show that the global South suffered disproportionately from the war in Ukraine's impact on wheat markets. We also analyze the effects of free-trade agreements and trade disputes with China, as well as Brexit's impact on British trade with Europe, uncovering hidden patterns that trade volumes alone cannot reveal.


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Abstract page for arXiv paper 2502.01640v1: Study on the impact of trade policy uncertainty on the performance of enterprise ESG performance


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Trade policy uncertainty has become a significant feature of today's global economy. While its impact on free trade is evident, its microeconomic effects remain open to debate. This study explores the influence of trade policy uncertainty on corporate ESG performance and its underlying mechanisms, using data from A-share listed companies in China from 2010 to 2020. The findings reveal that increased trade policy uncertainty significantly and robustly enhances corporate ESG performance. Heterogeneity analysis indicates that high-tech enterprises are better equipped to improve their ESG performance in response to trade policy uncertainty. Furthermore, strengthening internal controls and appointing CEOs with environmental backgrounds also help firms seize the opportunities arising from trade policy uncertainty. In terms of mechanisms, trade policy uncertainty intensifies industry competition, compelling firms to enhance their ESG performance to gain market share. Additionally, it stimulates green technological innovation, further optimizing ESG outcomes. Therefore, efforts should focus on improving the ESG standards system, establishing ESG incentive policies, increasing the transparency and predictability of trade policies, and promoting corporate green development to advance national sustainable development goals.


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Abstract page for arXiv paper 2405.14717v1: The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition


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The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition


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As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benets of domestic climate change mitigation and hydrogen exports, whereby exports can reduce domestic electricity prices while mitigation reduces hydrogen export prices. However, increasing hydrogen exports quickly in a system that is still dominated by fossil fuels can substantially raise domestic electricity prices, if green hydrogen production is not regulated. Surprisingly, temporal matching of hydrogen production lowers domestic electricity cost by up to 31% while the effect on exporters is minimal. This policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.


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In the early days of our nation, tariffs, or taxes on imported goods, were a primary source of government revenue. Over time, however, as the nation’s economy and businesses matured, it became clear that it was both fairer for American households and better for businesses, many of whom increasingly imported inputs to aid their domestic…


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/cea/written-materials/2024/07/12/tariffs-as-a-major-revenue-source-implications-for-distribution-and-growth/


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Abstract page for arXiv paper 2211.13117v1: On the Empirical Association between Trade Network Complexity and Global Gross Domestic Product


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In recent decades, trade between nations has constituted an important component of global Gross Domestic Product (GDP), with official estimates showing that it likely accounted for a quarter of total global production. While evidence of association already exists in macro-economic data between trade volume and GDP growth, there is considerably less work on whether, at the level of individual granular sectors (such as vehicles or minerals), associations exist between the complexity of trading networks and global GDP. In this paper, we explore this question by using publicly available data from the Atlas of Economic Complexity project to rigorously construct global trade networks between nations across multiple sectors, and studying the correlation between network-theoretic measures computed on these networks (such as average clustering coefficient and density) and global GDP. We find that there is indeed significant association between trade networks' complexity and global GDP across almost every sector, and that network metrics also correlate with business cycle phenomena such as the Great Recession of 2007-2008. Our results show that trade volume alone cannot explain global GDP growth, and that network science may prove to be a valuable empirical avenue for studying complexity in macro-economic phenomena such as trade.


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Abstract page for arXiv paper 2104.05754v2: The role of relatedness and strategic linkages between domestic and MNE sectors in regional branching and resilience


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The role of relatedness and strategic linkages between domestic and MNE sectors in regional branching and resilience


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Despite the key role of multinational enterprises (MNEs) in both international markets and domestic economies, there is no consensus on their impact on their host economy. In particular, do MNEs stimulate new domestic firms through knowledge spillovers? Here, we look at the impact of MNEs on the entry and exit of domestic industries in Irish regions before, during, and after the 2008 Financial Crisis. Specifically, we are interested in whether the presence of MNEs in a region results in knowledge spillovers and the creation of new domestic industries in related sectors. To quantify how related an industry is to a region's industry basket we propose two cohesion measures, weighted closeness and strategic closeness, which capture direct linkages and the complex connectivity structure between industries in a region respectively. We use a dataset of government-supported firms in Ireland (covering 90% of manufacturing and exporting) between 2006-2019. We find that domestic industries are both more likely to enter and less likely to leave a region if they are related to so-called 'overlapping' industries containing both domestic and MNE firms. In contrast, we find a negative impact on domestic entry and survival from cohesion to 'exclusive MNE' industries, suggesting that domestic firms are unable to 'leap' and thrive in MNE-proximate industries likely due to a technology or know-how gap. This dynamic was broken, with domestic firms entering MNE exclusive sectors, by a large injection of Brexit diversification funds in 2017-18. Finally, the type of cohesion matters. For example, strategic rather than weighted closeness to exclusive domestic sectors matters for both entries and exits.


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To implement an effective industrial policy, the United States needs to update its trade enforcement toolkit to meet the challenges of the modern world and utilize its existing trade authorities differently.


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Abstract page for arXiv paper 2210.09690v1: Electricity grid tariffs for electrification in households: Bridging the gap between cross-subsidies and fairness


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Electricity grid tariffs for electrification in households: Bridging the gap between cross-subsidies and fairness


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Developing new electricity grid tariffs in the context of household electrification raises old questions about who pays for what and to what extent. When electric vehicles (EVs) and heat pumps (HPs) are owned primarily by households with higher financial status than others, new tariff designs may clash with the economic argument for efficiency and the political arguments for fairness. This article combines tariff design and redistributive mechanisms to strike a balance between time-differentiated signals, revenue stability for the utility, limited grid costs for vulnerable households, and promoting electrification. We simulate the impacts of this combination on 1.4 million Danish households (about 50% of the country's population) and quantify the cross-subsidization effects between groups. With its unique level of detail, this study stresses the spillover effects of tariffs. We show that a subscription-heavy tariff associated with a ToU rate and a low redistribution factor tackles all the above goals.


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Abstract page for arXiv paper 2405.08052v1: Trade Openness, Tariffs and Economic Growth: An Empirical Study from Countries of G-20


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International trade has been in the forefront of economic development and growth debates. Trade openness, its definition, scope, and impacts have also been studied numerously. Tariff has been dubbed as negative influencer of economic growth as per conventional wisdom and most empirical studies. This paper empirically examines relationships among trade openness as trade share to GDP, import tariff rate and economic growth. Panel dataset of 11 G-20 member countries were selected for the study. Results found a positively significant correlation between trade openness and economic growth. Tariff has negatively significant correlation with economic growth in lagged model. OLS and panel data fixed-effects regression were employed to carry out the regression analysis. To deal with endogeneity in trade openness variable, a 1-year lag regression technique was conducted. Results are robust and significant. Policy recommendation suggests country specific trade opening and tariff relaxation.


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Abstract page for arXiv paper 2012.02966v1: Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland


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Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-in-differences estimation using a novel dataset of weekly producer prices for Switzerland and neighbouring countries. We find that TRQs increase prices of most vegetables by more than 20% above the prices in neighbouring countries during the main harvest time for most vegetables and even more than 50% for some vegetables. The effects are stronger for more perishable vegetables and for conventionally produced ones compared with organic vegetables. However, we do not find clear-cut effects of TRQs on the week-to-week price volatility of vegetables although the overall lower price volatility in Switzerland compared with neighbouring countries might be a result of the TRQ system in place.


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Abstract page for arXiv paper 2303.04433v1: Balancing DSO interests and PV system economics with alternative tariffs


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Distributed rooftop photovoltaics (PV) is one of the pillars of the energy transition. However, the massive integration of distributed PV systems challenges the existing grid, with high amounts of PV injection possibly leading to over-voltage and reverse power flow, with line and transformer overloading, among other issues. Moreover, the increase in PV self-consumption and consequently the reduction of imported electricity poses a problem in recovering Transmission System Operators (TSOs) and Distribution System Operators (DSOs) grid costs, that until now have been directly linked to the amount of electricity consumed due to the volumetric nature of traditional tariffs. To investigate whether alternative tariffs could mitigate PV impacts at the distribution level without hampering PV development, we assess five electricity tariffs that could help the DSOs to recover the costs of maintaining the distribution grid. Additionally, we evaluate how such tariffs may affect private investment in storage and their impact on three types of low-voltage networks (i.e., urban, semi-urban, and rural). We found that tariffs with a capacity-based component promote further adoption of PV and storage. At the same time, they allow the DSOs to recover the grid cost without incurring relevant economic differences for the customer. However, all assessed tariffs were found to have a limited role in mitigating PV impacts at the distribution level.


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The empirical evidence on the growth effects of import tariffs is sparse in the literature, notwithstanding strong views held by the public and politicians. Using an annual panel of macroeconomic data for 151 countries over 1963–2014, we find that ...


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Abstract page for arXiv paper 1706.09365v2: Bilateral multifactor CES general equilibrium with state-replicating Armington elasticities


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We measure elasticity of substitution between foreign and domestic commodities by two-point calibration such that the Armington aggregator can replicate the two temporally distant observations of market shares and prices. Along with the sectoral multifactor CES elasticities which we estimate by regression using a set of disaggregated linked input--output observations, we integrate domestic production of two countries, namely, Japan and the Republic of Korea, with bilateral trade models and construct a bilateral general equilibrium model. Finally, we make an assessment of a tariff elimination scheme between the two countries.


