Family Caregivers' Subjective Caregiving Burden,Quality of Life,and Depressive Symptoms Are Associated With Terminally Ill Cancer Patients' Distinct Patterns of Conjoint Symptom Distress and Functional Impairment in Their Last Six Months of Life

Context

Family caregivers constitute a critical component of the end-of-life care system with considerable cost to themselves. However, the joint association of terminally ill cancer patients' symptom distress and functional impairment with caregivers' subjective caregiving burden, quality of life (QOL), and depressive symptoms remains unknown.

Next-generation sequencing through multigene panel testing for the diagnosis of hereditary epidermolysis bullosa in Chinese population

Epidermolysis bullosa (EB) is a heritable blistering disorder. We performed a next-generation sequencing-based multigene panel test and successfully predicted 100% of the EB types, including, 36 EB simplex (EBS), 13 junctional EB (JEB), 86 dystrophic EB (DEB), and 3 Kindler EB. Chinese JEB and recessive DEB (RDEB) patients have relatively mild phenotypes; for severe type separately accounts for 45.5% and 23.8%, respectively. We identified 96 novel and 49 recurrent pathogenic variants in 11 genes, although we failed to detect the second mutation in one JEB and five RDEB patients. We identified one novel p.E475K mosaic mutation in the clinically normal mother of one out of 13 EBS patients with KRT5 mutations, one recurrent p.G2034R mosaic mutation, and one novel p.G2043R mosaic mutation in the clinically normal relatives of two out of 19 dominant DEB patients. This study shows that next-generation technology could be an effective tool in diagnosing EB.     

互动式歌唱表演对轻中度阿尔茨海默病患者抑郁、精神行为和运动训练参与率的影响

目的 观察互动式歌唱表演对轻中度阿尔茨海默病（AD）患者抑郁、精神行为症状及运动训练参与率的影响。方法 选取符合入组条件≥60周岁AD患者63例，随机分为研究组（31例）和对照组（32例）。所有受试患者常规药物治疗及常规运动训练，对照组接受被动性音乐治疗，研究组接受以互动歌唱为主的主动性音乐治疗，1次/d，每次1小时，每周训练5天，持续干预6个月。于治疗前、治疗1个月后、治疗3个月后、治疗6个月后分别采用康奈尔痴呆抑郁量表（CSDD）评分、阿尔茨海默病病理行为（BEHAVE AD）评分、参与率进行评估。结果 治疗1个月、3个月后，研究组CSDD评分较治疗前均降低（P<0.05）；治疗6个月后，研究组患者CSDD评分较治疗前、治疗1个月、3个月后均显著降低（P<0.05），且与对照组比较差异有统计学意义（P<0.05）。治疗1个月、3个月后，研究组BEHAVE AD评分较治疗前均降低（P<0.05）；治疗6个月后，研究组患者BEHAVE AD评分较治疗前、治疗1个月、3个月后均显著降低（P<0.05），且与对照组比较差异有统计学意义（P<0.05）。治疗6个月后，两组运动训练参与率组间比较差异有统计学意义（P<0.05）。结论 互动式歌唱表演可能对改善轻中度AD患者的抑郁和精神行为症状有着积极的疗效，同时对提高受试者运动训练的参与率可能有着更积极的疗效。     

Loss of function,missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and noncoding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and noncoding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based data sets in quantifying the specific risk of individual variants for disease-related phenotypes.     

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.