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。     

H型高血压患者舌面诊图像颜色特征参数分析＊

目的 分析H型高血压患者的舌面诊图像颜色参数特征，探讨H型高血压患者的舌诊、面诊变化规律。方法 运用上海中医药大学自行研制的Smart TCM-1型中医舌面一体仪，采集高血压患者舌面诊图像，提取特征参数，分析健康对照组、H型高血压组与非H型高血压组患者舌面颜色参数特征。结果 ①在舌色各项参数中，H型高血压组舌尖部R值、B值、V值均显著小于健康对照组（P < 0.01）；非H型高血压组舌尖部B值显著小于健康对照组（P < 0.01），S值较健康对照组显著增大（P < 0.05）；H型高血压组舌尖部R、V值均明显小于非H型高血压组（P < 0.05）。在舌苔各项参数中，H型高血压组舌中H值、V值均明显小于健康对照组（P < 0.05）；非H型高血压组舌中V值、舌右V值均显著小于健康对照组（P < 0.01）；H型高血压组舌中H值明显小于非H型高血压组（P < 0.05），右侧舌苔S值明显大于非H型高血压组（P < 0.05）。②H型高血压组面色参数鼻G值、下颌G值、口唇R值、口唇V值均明显小于健康对照组（P < 0.05）；非H型高血压组前额H值、目眶H值、脸颊H值、鼻H值、下颌H值、整体H值均明显大于健康对照组（P < 0.05）；H型高血压组前额H值、目眶G值、目眶H值、脸颊H值、鼻G值、鼻H值、下颌R值、下颌G值、下颌H值、下颌V值、口唇R值、口唇G值、口唇V值、整体R值、整体G值、整体H值、整体V值均明显小于非H型高血压组（P < 0.05）。结论 H型高血压患者苔色偏黄，以舌中部为主，且舌右侧黄苔积聚较明显；H型高血压患者面色为黄中带红，口唇、下颌部更为晦暗。H型高血压患者的舌、面诊特征参数的变化，与高血压病阳亢湿盛病机相符。     

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.     

Role of macrophages in peripheral nerve injury and repair

Resident and inflammatory macrophages are essential effectors of the innate immune system. These cells provide innate immune defenses and regulate tissue and organ homeostasis. In addition to their roles in diseases such as cancer, obesity and osteoarthritis, they play vital roles in tissue repair and disease rehabilitation. Macrophages and other inflammatory cells are recruited to tissue injury sites where they promote changes in the microenvironment. Among the inflammatory cell types, only macrophages have both pro-inflammatory(M1) and anti-inflammatory(M2) actions, and M2 macrophages have four subtypes. The co-action of M1 and M2 subtypes can create a favorable microenvironment, releasing cytokines for damaged tissue repair. In this review, we discuss the activation of macrophages and their roles in severe peripheral nerve injury. We also describe the therapeutic potential of macrophages in nerve tissue engineering treatment and highlight approaches for enhancing M2 cell-mediated nerve repair and regeneration.     

放射性心脏损伤动物模型相关实验研究进展

当利用放射线对胸部恶性肿瘤进行治疗时，位于纵隔的心脏会不可幸免受到照射，从而诱发放射性心脏损伤（radiation-induced heart disease, RIHD）。随着手术以及放化疗技术的提升，肿瘤患者生存时间得到延长，使得RIHD这一放疗远期并发症被越来越多的报道。因此，学者们对于RIHD的研究逐渐升温。目前国内外学者关于该疾病尚未形成统一的认识，临床上缺乏有效阻止其发生的方法。动物模型研究可为临床该疾病治疗及预防提供可靠证据，为此本文回顾分析近年来放射性心脏损伤动物模型实验研究情况，旨在为后续实验开展及临床应用提供参考。     

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.