G6PD Deficiency Is Crucial for Insulin Signaling Activation in Skeletal Muscle

Glucose 6-P dehydrogenase (G6PD) is the first rate-limiting enzyme in pentose phosphate pathway (PPP), and it is proverbial that G6PD is absent in skeletal muscle. However, how and why G6PD is down-regulated during skeletal muscle development is unclear. In this study, we confirmed the expression of G6PD was down-regulated during myogenesis in vitro and in vivo. G6PD was absolutely silent in adult skeletal muscle. Histone H3 acetylation and DNA methylation act together on the expression of G6PD. Neither knock-down of G6PD nor over-expression of G6PD affects myogenic differentiation. Knock-down of G6PD significantly promotes the sensitivity and response of skeletal muscle cells to insulin; over-expression of G6PD significantly injures the sensitivity and response of skeletal muscle cells to insulin. High-fat diet treatment impairs insulin signaling by up-regulating G6PD, and knock-down of G6PD rescues the impaired insulin signaling and glucose uptake caused by high-fat diet treatment. Taken together, this study explored the importance of G6PD deficiency during myogenic differentiation, which provides new sight to treat insulin resistance and type-2 diabetes

STOPS: Short-Term-based Volatility-controlled Policy Search and its Global Convergence

It remains challenging to deploy existing risk-averse approaches to real-world applications. The reasons are multi-fold, including the lack of global optimality guarantee and the necessity of learning from long-term consecutive trajectories. Long-term consecutive trajectories are prone to involving visiting hazardous states, which is a major concern in the risk-averse setting. This paper proposes Short-Term VOlatility-controlled Policy Search (STOPS), a novel algorithm that solves risk-averse problems by learning from short-term trajectories instead of long-term trajectories. Short-term trajectories are more flexible to generate, and can avoid the danger of hazardous state visitations. By using an actor-critic scheme with an overparameterized two-layer neural network, our algorithm finds a globally optimal policy at a sublinear rate with proximal policy optimization and natural policy gradient, with effectiveness comparable to the state-of-the-art convergence rate of risk-neutral policy-search methods. The algorithm is evaluated on challenging Mujoco robot simulation tasks under the mean-variance evaluation metric. Both theoretical analysis and experimental results demonstrate a state-of-the-art level of STOPS' performance among existing risk-averse policy search methods

How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov

In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.We analyzed 3765 COVID-19 studies registered in the largest public registry—ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population.Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies.Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability