儿童呼吸道合胞病毒感染诊断、治疗和预防专家共识

呼吸道合胞病毒(respiratory syncytial virus,RSV)是世界范围内引起5岁以下儿童急性下呼吸道感染(acute lower respiratory tract infections,ALRTI)最重要的病毒病原[1]。RSV感染是造成婴幼儿病毒性呼吸道感染住院的首要因素,严重危害儿童健康,尤其对早产儿、患有先天性心脏病或原发免疫缺陷的婴幼儿造成的疾病更重。目前,尚无RSV疫苗及有效的抗病毒药物用于RSV的治疗,唯一可用于RSV预防的人源化特异性抗体帕利珠单抗(Palivizumab)尚未引进国内临床应用。临床上在RSV的流行、致病机制、诊断、治疗及预防等方面尚存在一些不足,为进一步规范儿童RSV感染的诊断、治疗及预防,以国内外RSV最新研究进展为参考,特制定此专家共识。     

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。     

INAUGURAL ARTICLE by a Recently Elected Academy Member:Functional redundancy of type I and type II receptors in the regulation of skeletal muscle growth by myostatin and activin A

Myostatin (MSTN) is a transforming growth factor-β (TGF-β) family member that normally acts to limit muscle growth. The function of MSTN is partially redundant with that of another TGF-β family member, activin A. MSTN and activin A are capable of signaling through a complex of type II and type I receptors. Here, we investigated the roles of two type II receptors (ACVR2 and ACVR2B) and two type I receptors (ALK4 and ALK5) in the regulation of muscle mass by these ligands by genetically targeting these receptors either alone or in combination specifically in myofibers in mice. We show that targeting signaling in myofibers is sufficient to cause significant increases in muscle mass, showing that myofibers are the direct target for signaling by these ligands in the regulation of muscle growth. Moreover, we show that there is functional redundancy between the two type II receptors as well as between the two type I receptors and that all four type II/type I receptor combinations are utilized in vivo. Targeting signaling specifically in myofibers also led to reductions in overall body fat content and improved glucose metabolism in mice fed either regular chow or a high-fat diet, demonstrating that these metabolic effects are the result of enhanced muscling. We observed no effect, however, on either bone density or muscle regeneration in mice in which signaling was targeted in myofibers. The latter finding implies that MSTN likely signals to other cells, such as satellite cells, in addition to myofibers to regulate muscle homeostasis.

Myostatin (MSTN) is a secreted signaling molecule that normally acts to limit skeletal muscle growth (for review, see ref. 1). Mice lacking MSTN exhibit dramatic increases in muscle mass throughout the body, with individual muscles growing to about twice the normal size (2). MSTN appears to play two distinct roles in regulating muscle size, one to regulate the number of muscle fibers that are formed during development and a second to regulate the growth of those fibers postnatally. The sequence of MSTN has been highly conserved through evolution, with the mature MSTN peptide being identical in species as divergent as humans and turkeys (3). The function of MSTN has also been conserved, and targeted or naturally occurring mutations in MSTN have been shown to cause increased muscling in numerous species, including cattle (3–5), sheep (6), dogs (7), rabbits (8), rats (9), swine (10), goats (11), and humans (12). Numerous pharmaceutical and biotechnology companies have developed biologic agents capable of blocking MSTN activity, and these have been tested in clinical trials for a wide range of indications, including Duchenne and facioscapulohumeral muscular dystrophy, inclusion body myositis, muscle atrophy following falls and hip fracture surgery, age-related sarcopenia, Charcot–Marie–Tooth disease, and cachexia due to chronic obstructive pulmonary disease, end-stage kidney disease, and cancer.The finding that certain inhibitors of MSTN signaling can increase muscle mass even in Mstn^−/− mice revealed that the function of MSTN as a negative regulator of muscle mass is partially redundant with at least one other TGF-β family member (13, 14), and subsequent studies have identified activin A as one of these cooperating ligands (15, 16). MSTN and activin A share many key regulatory and signaling components. For example, the activities of both MSTN and activin A can be modulated extracellularly by naturally occurring inhibitory binding proteins, including follistatin (17, 18) and the follistatin-related protein, FSTL-3 or FLRG (19, 20). Moreover, MSTN and activin A also appear to share receptor components. Based on in vitro studies, MSTN is capable of binding initially to the activin type II receptors, ACVR2 and ACVR2B (also called ActRIIA and ActRIIB) (18) followed by engagement of the type I receptors, ALK4 and ALK5 (21). In previous studies, we presented genetic evidence supporting a role for both ACVR2 and ACVR2B in mediating MSTN signaling and regulating muscle mass in vivo. Specifically, we showed that mice expressing a truncated, dominant-negative form of ACVR2B in skeletal muscle (18) or carrying deletion mutations in Acvr2 and/or Acvr2b (13) have significantly increased muscle mass. One limitation of the latter study, however, was that we could not examine the consequence of complete loss of both receptors using the deletion alleles, as double homozygous mutants die early during embryogenesis (22). Moreover, the roles that the two type I receptors, ALK4 and ALK5, play in regulating MSTN and activin A signaling in muscle in vivo have not yet been documented using genetic approaches. Here, we present the results of studies in which we used floxed alleles for each of the type II and type I receptor genes in order to target these receptors alone and in combination in muscle fibers. We show that these receptors are functionally redundant and that signaling through each of these receptors contributes to the overall control of muscle mass.     

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.