Dietary menthol-induced TRPM8 activation enhances WAT “browning” and ameliorates diet-induced obesity

Beige adipocytes are a new type of recruitable brownish adipocytes, with highly mitochondrial membrane uncoupling protein 1 expression and thermogenesis. Beige adipocytes were found among white adipocytes, especially in subcutaneous white adipose tissue (sWAT). Therefore, beige adipocytes may be involved in the regulation of energy metabolism and fat deposition. Transient receptor potential melastatin 8 (TRPM8), a Ca2+-permeable non-selective cation channel, plays vital roles in the regulation of various cellular functions. It has been reported that TRPM8 activation enhanced the thermogenic function of brown adiposytes. However, the involvement of TRPM8 in the thermogenic function of WAT remains unexplored. Our data revealed that TRPM8 was expressed in mouse white adipocytes at mRNA, protein and functional levels. The mRNA expression of Trpm8 was significantly increased in the differentiated white adipocytes than pre-adipocytes. Moreover, activation of TRPM8 by menthol enhanced the expression of thermogenic genes in cultured white aidpocytes. And menthol-induced increases of the thermogenic genes in white adipocytes was inhibited by either KT5720 (a protein kinase A inhibitor) or BAPTA-AM. In addition, high fat diet (HFD)-induced obesity in mice was significantly recovered by co-treatment with menthol. Dietary menthol enhanced WAT "browning" and improved glucose metabolism in HFD-induced obesity mice as well. Therefore, we concluded that TRPM8 might be involved in WAT "browning" by increasing the expression levels of genes related to thermogenesis and energy metabolism. And dietary menthol could be a novel approach for combating human obesity and related metabolic diseases

Rodent models of postherpetic neuralgia: How far have we reached?

BackgroundInduced by varicella zoster virus (VZV), postherpetic neuralgia (PHN) is one of the common complications of herpes zoster (HZ) with refractory pain. Animal models play pivotal roles in disclosing the pain mechanisms and developing effective treatments. However, only a few rodent models focus on the VZV-associated pain and PHN.ObjectiveTo summarize the establishment and characteristics of popular PHN rodent models, thus offer bases for the selection and improvement of PHN models.DesignIn this review, we retrospect two promising PHN rodent models, VZV-induced PHN model and HSV1-induced PHN model in terms of pain-related evaluations, their contributions to PHN pathogenesis and pharmacology.ResultsSignificant difference of two PHN models is the probability of virus proliferation; 2) Most commonly used pain evaluation of PHN model is mechanical allodynia, but pain-induced anxiety and other behaviours are worth noting; 3) From current PHN models, pain mechanisms involve changes in virus gene and host gene expression, neuroimmune–glia interactions and ion channels; 4) antiviral drugs and classical analgesics serve more on the acute stage of herpetic pain.ConclusionsDifferent PHN models assessed by various pain evaluations combine to fulfil more comprehensive understanding of PHN

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Spatial and Temporal Variability of Upwelling in the West-Central South China Sea and Its Relationship with the Wind Field

The west-central South China Sea upwelling event is a critical process that regulates the climate and marine ecosystem in the region. In this study, we used sea surface temperature (SST) satellite data from 2000 to 2018 to analyze the spatial and temporal characteristics of upwelling in the west-central South China Sea and combined the wind field data to investigate the effects of wind direction and speed on upwelling. We divided the upwelling sea area into three regions based on the different shoreline angles along the eastern coast of the South China Peninsula: OU_1, OU_2, and OU_3. Our results showed that the upwelling events occurred mainly from May to September in the OU_1 and OU_2 waters. The empirical orthogonal function (EOF) decomposition of the monthly mean SST moment level field indicated a cyclical interannual variation of upwelling in the west-central South China Sea. The correlation analysis showed that wind direction changes have a significant impact on the upwelling intensity center, with the upwelling intensity center moving towards high latitudes and away from the coast when the wind direction changes from north to east. When the wind direction changes from east to south, the upwelling intensity center moves towards low latitudes and near the coast. The average lag time of upwelling events to the wind field in the central and western South China Sea was 38.9 h, with OU_2 showing a longer response time than the other seas. Our study provides important insights into the mechanisms governing upwelling in the west-central South China Sea, which can effectively promote the rational use of ecological resources and provide a scientific basis for marine ecological protection in the region

The Impact of Natural Product Dietary Supplements on Patients with Gout: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Natural product dietary supplements (NPDS) are frequently used for the treatment of gout, but reliable efficacy and safety data are generally lacking or not well organized to guide clinical decision making. This review aims to explore the impacts of NPDS for patients with gout. An electronic literature search was conducted to retrieve data published in English language from databases from inception to August 14, 2019. Randomized controlled trials (RCTs) that compared NPDS with or without placebo, diet modification, conventional pharmaceutics, or the other Chinese medicine treatment for gout patients were included. Two authors screened the articles, extracted the data, and assessed the risk of bias of each included trial independently. Meta-analysis was performed using Review Manager version 5.3.5. Results. Nine RCTS were enrolled in this review. The methodological quality of the nine RCTs was poor. The study results showed that in the majority of trials, NPDS demonstrated some degree of therapeutic efficacy for joint swelling, pain, and activity limitation. In contradistinction, serum uric acid (SUA) level (SMD −1.80, 95% CI: −4.45 to 0.86) (p>0.05) and CRP levels (N = 232; SMD, −0.26; 95% CI, −0.55 to 0.04) (p>0.05) did not improve significantly. The incidence of adverse events (AEs) was not lower in the participants treated with NPDS (N = 750; RR, 0.47; 95% CI, 0.20–1.11) (p>0.05). Conclusion. Current existing evidence is not sufficient to provide clinical guidance regarding the efficacy and safety of NPDS as a treatment for gout due to poor trial quality and lack of standardized evaluation criteria. Larger and more rigorously designed RCTs are needed in the future