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.     

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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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.     

角膜电刺激对糖尿病大鼠前部缺血性视神经病变模型的影响

观察并评估角膜电刺激对糖尿病大鼠前部缺血性视神经病变（AION）模型的影响。方法：实验 研究。健康雄性Sparague-Dawley大鼠40只，随机分组后抽出8只作为正常大鼠组。余下32只先予 以链脲佐菌素腹腔注射建立糖尿病大鼠模型，将造模成功的大鼠随机抽出8只作为糖尿病组，余下 24只糖尿病大鼠采用孟加拉玫瑰红联合532 nm激光方法建立AION大鼠模型。将24只造模成功的 AION大鼠随机分成3组，每组8只，分别为AION模型组，不予任何处理；电刺激组，予以角膜电刺 激（刺激参数为：电流1 mA，频率20 Hz，波宽1 ms/phase，刺激时间1 h，隔日1次，刺激2周）；假电 刺激组，电极安放位置与电刺激组相同，仅不接通电源。2周后5组大鼠进行眼底照相、光学相干断 层扫描和视觉诱发电位，然后处死，行视网膜及视神经冰冻切片，苏木精伊红染色观察。数据采用 单因素方差分析和LSD-t检验进行分析。结果：正常大鼠组视盘上半部视网膜厚度为（211±13）μm， 糖尿病大鼠组为（206±16）μm，AION模型组为（240±54）μm，假电刺激组为（216±11）μm，电刺 激组为（198±4）μm，5组视盘上半部视网膜厚度差异有统计学意义（F=2.854，P=0.038）。其中AION 模型组视盘上半部视网膜厚度高于正常组、糖尿病组、电刺激组，差异均有统计学意义（P<0.05）； 正常组与糖尿病组差异无统计学意义，AION模型组与假电刺激组未见明显差异。视觉诱发电位示 AION模型组N1潜伏期较电刺激组延长，差异有统计学意义（t=4.1，P<0.001）；AION模型组P1潜伏 期较正常组、糖尿病组、假电刺激组、电刺激组延长，差异均有统计学意义（t=4.1、2.5、2.6、3.2， P<0.05）；电刺激组N1-P1波幅大于假电刺激组，差异有统计学意义（t=4.0，P<0.001）。结论：角膜电 刺激能促进糖尿病大鼠前部缺血性视神经病变模型肿胀的视盘变薄，加速视盘水肿的消退，同时在 一定程度上改善视功能。