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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Differential Diagnosis of Acute Diarrhea

Acute diarrhea is a condition of increased water stool content, stool volume, and number of bowel movements that lasts less than 14 days. Mild diarrhea is usually self-limiting; however, undertreated moderate to severe diarrhea may cause severe dehydration and lead to hypovolemic shock. In order to prevent severe dehydration and treat patients appropriately, it is crucial for health care providers to determine the right diagnosis of patients with acute diarrhea. This article focuses on pathophysiology, general patient presentation, diagnostic tests and differential diagnosis lists of acute diarrhea to discuss which diagnosis should be made based on patient presentation and objective data.     

流动人口基层医疗卫生机构首诊现状及影响因素分析

目的:了解流动人口基层首诊现状及其影响因素,为推进流动人口分级诊疗提供实证参考依据。方法:基于2017年全国流动人口动态监测调查数据中82734名最近1次患病(负伤)流动人口数据,利用SPSS 25.0统计软件分析其基层首诊情况及影响因素。结果:82734名最近1次患病(负伤)流动人口中首选到基层医疗卫生机构就诊15936人,基层首诊率仅为19.3%;二分类logistic回归分析结果显示:年龄≥65岁、农业户口、流动时间0~5年、患慢性病、至少参加1项医疗保险,居住地到最近医疗服务机构所需时间≤15 min的流动人口患病后更愿意选择到基层首诊。学历大专及以上、家庭月均收入>10000元、市跨县、东北地区、不愿意落户、自评健康状况为不健康的流动人口患病后更不愿意选择到基层首诊。结论:流动人口患病(负伤)后选择到基层首诊率较低,年龄、受教育程度、户口类型、家庭月均总收入、流动时间、流动范围、流入地区域、落户意愿、自评健康状况、是否患慢性病、有无参加医疗保险、居住地到最近医疗服务机构所需时间是影响流动人口患病(负伤)后选择到基层首诊的主要因素。     

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.