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。     

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.     

Psychosocial mechanisms of a behavioral treatment for urinary incontinence of prostate cancer survivors

Abstract

Purpose: We examined underlying psychosocial processes of a behavioral treatment for urinary incontinence (UI) of prostate cancer survivors.

Design: Secondary analysis of data collected from a clinical trial.

Sample: Two hundred forty-four prostate cancer survivors who participated in a clinical trial of behavioral intervention to UI as intervention or control subjects.

Methods: The participants had a 3-month behavioral intervention or usual care and were followed up for an additional 3?months. They were assessed at baseline, 3, and 6?months. Latent growth curve models were performed to examine trajectories of each study variable and relationships among the variables.

Findings: Increasing self-efficacy and social support were significantly and independently associated with more reduction of urinary leakage frequency over time.

Implications for psychosocial oncology: Providing problem-solving skills and social support, including peer support, are essential for empowering patients to reduce UI.     

Multi-day radon signals with a radioactive decay limb -- occurrence and geophysical significance.

Multi-day signals, generally with duration of 2-10 days, are a prominent temporal variation type of radon (Rn) in geogas in the unsaturated zone. Rare multi-day Rn signals have been found which are characterized by: (a) a declining limb lasting up to 10 days which conforms to the radioactive decay of Rn, (b) recurs at the same location and (c) is recorded in diverse situations-volcanic and seismogenic. It suggested that a Rn blob is injected at a lower level on a steady upward flow of geogas whereby the rise and final fall of the signal are attributed to the edges of the blob while the central Rn-decay segment records the passing of the decaying blob itself. Rn-decay signals are a small subset of multi-day Rn signals which are considered as highly irregular and unusable for the understanding of geophysical processes. In difference, it is concluded that multi-day Rn signals are probably proxies of subtle geodynamic processes at upper crustal levels and are therefore significant for studying such processes.