2‐deoxy D‐glucose treatment does not elicit a hair growth response in alopecia areata

2‐deoxy D‐glucose (2DG) was tested for efficacy in treating alopecia areata using the C3H/HeJ skin graft model. 2DG has proven to be efficacious in treatment of various mouse models of autoimmunity with minimal serious side effects noted. This agent has been shown to normalize abnormally activated T‐cell populations while also preventing cell surface expression of NKG2D; key factors defining alopecia areata disease progression. Daily oral ingestion of 2DG via drinking water to mice with patchy or diffuse alopecia areata for 16 weeks failed to prevent expansion of alopecia or cause regrowth of hair in treated mice. Histologically, there were no differences between treated and control groups. These results indicate that, while 2DG is effective for some autoimmune diseases, it was not efficacious for the cell‐mediated autoimmune mouse disease, alopecia areata.     

Worsening head bleeds in elderly blunt head trauma patients taking antithrombotics: Delayed CT head fails to change management

Background

Most elderly trauma patients suffer blunt head injury and many utilize antithrombotic (AT) medications. The utility of delayed CT-head (D-CTH) in neurologically intact elderly patients using AT who have an intracranial hemorrhage (ICH) on presentation is unknown. We hypothesized that D-CTH would not alter clinical management and aimed to evaluate the role of D-CTH in this population.

沙库巴曲缬沙坦在心力衰竭治疗中的应用

心力衰竭是一种慢性疾病，是心脏无法泵出足够的血液为其他身体器官提供氧气，从而引发呼吸短促、疲劳和水肿等症状。目前其治疗包括病因治疗、一般治疗、药物治疗及非药物治疗等，其中血管紧张素受体脑啡肽酶抑制剂（ARNI）是当前新研发出的一类抗心衰药物，沙库巴曲缬沙坦是其代表药物，具有阻断肾素-血管紧张-醛固酮系统（RAAS）及抑制脑啡肽酶（NEP）的双重作用，新的欧美指南均推荐用于射血分数降低心力衰竭（HFrEF）的治疗。本文现就沙库巴曲缬沙坦在心力衰竭治疗中的作用机理、临床试验、药物安全性作一综述，旨在为该病的临床治疗提供参考。     

Do neuronal microvascular activation and resultant dysfunction in rheumatoid arthritis contribute to chronic pain?

<正>Microvascular dysfunction and rheumatoid arthritis(RA):Across the UK severe osteoarthritis affects～5–7%and RA affects～1%of the population.These are the most common causes of disabling chronic pain and are major burdens on individuals and society.Consequently,the annual financial cost to the UK economy for arthritis is in the billions of pounds.People living with painful inflammatory conditions and in     

偏瘫性肩痛患者腋囊的MRI观测及临床意义

目的通过MRI技术观察偏瘫患者肩疼痛腋囊形态变化,为影像诊断提供依据。方法对47例偏瘫性肩疼痛患者(疼痛组)和47例正常肩部(对照组),运用MRI技术对肩关节腋囊进行测量,观察腋囊下肱盂韧带信号。结果偏瘫性肩疼痛的腋囊厚度(4.1±1.45)mm,高于对照组。腋囊腔高度(8.70±1.80)mm,腋囊腔宽度(4.31±0.56)mm,小于对照组。偏瘫性肩痛下肱盂韧带呈高信号出现率达21%,腋囊厚度与VAS呈正相关,与肩关节外展外旋呈负相关。结论脑中风中后期肩周炎是引起偏瘫性肩疼痛患者的常见原因,腋囊厚度是限制肩关节活动度的重要因素。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

The Course of Eating Disorders Involving Bingeing and Purging Among Adolescent Girls: Prevalence,Stability, and Transitions

Purpose

To quantify eating disorder (ED) stability and diagnostic transition among a community-based sample of adolescents and young adult females in the United States.