Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Prolyl and lysyl hydroxylases in collagen synthesis

Collagens are the most abundant proteins in the extracellular matrix. They provide a framework to build organs and tissues and give structural support to make them resistant to mechanical load and forces. Several intra‐ and extracellular modifications are needed to make functional collagen molecules, intracellular post‐translational modifications of proline and lysine residues having key roles in this. In this article, we provide a review on the enzymes responsible for the proline and lysine modifications, that is collagen prolyl 4‐hydroxylases, 3‐hydroxylases and lysyl hydroxylases, and discuss their biological functions and involvement in diseases.     

基于网络药理学和分子对接技术研究黄芪-赤芍治疗COPD的作用机制＊

目的 基于网络药理学和分子对接技术探究黄芪-赤芍配伍对治疗慢性阻塞性肺疾病（chronic obstructive pulmonary disease，COPD）的作用机制。方法 利用TCMSP，Pharmmaper数据库，筛选黄芪-赤芍治疗COPD的活性成分和潜在靶点；结合Genecards数据库挖掘的COPD相关靶点，对黄芪-赤芍药对与COPD靶点进行PPI网络构建，交互处理得到黄芪-赤芍药对治疗COPD的关键靶点，并进行GO分析和KEGG通路富集分析；并采用分子对接技术将主要活性成分与TNF-α（肿瘤坏死因子），IL-6（白细胞介素6）等进行分子对接；最后利用A549炎症细胞与人脐静脉内皮细胞缺氧损伤模型进行体外细胞实验对结果加以验证。结果 黄芪-赤芍药对中44个有效成分作用于COPD，核心成分为：槲皮素、山奈酚、丁子香萜、芍药苷、（2R，3R）-4-methoxyl-distylin、二氢异黄酮；黄芪-赤芍药对通过IL6、PTGS2、TNF等113个靶蛋白，调控Ras、PI3KAkt、IL-17等多条信号通路治疗COPD，且分子对接结果显示槲皮素、山奈酚、丁子香萜、芍药苷与IL-6、PTGS2、TNF大分子蛋白有良好的结合性，体外细胞试验证实，槲皮素与山奈酚均能减少IL-8，MMP-9炎症因子的分泌，具有不同程度的抗炎效果；芍药苷有明显的扩血管、抗血栓之效。结论 黄芪-赤芍药对治疗COPD具有多成分、多靶点、多通路、整体调节的作用特点。初步揭示了黄芪-赤芍药对通过抑制炎症反应、调节上皮细胞生长增强保护屏障等预测出黄芪-赤芍药对治疗COPD的潜在作用机制，以期为其活性成分的药效物质基础提供理论研究和思路。     

我国家庭医生签约服务政策执行的制约因素与优化路径：基于史密斯政策执行过程模型

深化家庭医生签约服务是深化医药卫生体制改革、强化基层医疗卫生服务、实现"健康中国"战略目标的重要选择，也是当前更好维护人民群众健康的重要途径。为有效推进签约服务工作，国家陆续推出各项政策，全国各地也在积极进行实践探索，成效明显。但是，签约服务仍面临诸多问题，其中"执行难"是签约服务深度推进的一大困境。通过史密斯政策执行过程模型，结合签约服务政策执行过程，发现签约服务仍存在法治性不足、政策执行人员水平不高、激励不足、政策环境影响等诸多制约因素。因此，需要从法律和制度方面进行顶层设计、提升执行人员素质和职业认同、建立医患互信、优化政策执行环境等角度进行政策创新，探索家庭医生签约服务可持续发展的路径。     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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国外动物智能军事应用情报研究

目的：阐述动物智能在军事学的应用及意义。方法：以学术论文、新闻、报纸、智库报告等作为信息源，综合研究国外动物智能军事应用实例及内在关联，描述动物智能在未来战场应用场景。结果：动物智能军事应用主要是动物特殊能力的军事应用和动物智能的仿生武器应用。结论：动物部队、仿生作战部队和动物战术是未来战争不可忽视的重要组成部分，动物智能的隐匿、灵活、多变等特点正逐渐被广泛应用于战场。     

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

ObjectivesSurvival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared.MethodsThree different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates.ResultsStandard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model).ConclusionsWhere a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.