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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基于DTI评价急性缺血性脑卒中运动功能缺损程度与肾虚髓亏证的相关性研究＊

目的 运用核磁共振弥散张量成像技术（DTI），从皮质脊髓束损伤程度评价的角度，探讨肾虚髓亏证与急性缺血性脑卒中运动功能缺损程度相关性，丰富中风病病机及证候诊断，拓宽缺血性脑卒中的中医药防治思路，为急性缺血性脑卒中肾虚髓亏证患者运动功能损伤程度提供临床依据，强调肾虚髓亏证在急性缺血性脑卒中运动功能损伤中的重要意义。方法 纳入符合诊断标准的90例病例，根据证候分别归入肾虚髓亏组和非肾虚髓亏组，每组各45例。每组患者均给予常规西药治疗。对两组患者入院后行弥散张量成像检测，同时分别于治疗前及治疗后14天，记录两组患者NIHSS评分、改良Barthel指数量表及简化Fugl-Meyer运动功能评分量表评分，比较两组病例发病时的轻重程度及治疗前后两组病例组间的恢复差异。结果 研究显示，治疗前肾虚髓亏组在NIHSS评分方面高于非肾虚髓亏组（P<0.05）；治疗前肾虚髓亏组在改良Barthel指数量表及简化Fugl-Meyer运动功能评分方面低于非肾虚髓亏组（P<0.05）。治疗前后NIHSS评分、改良Barthel指数评分及简化Fugl-Meyer运动功能评分改善情况，非肾虚髓亏组要优于肾虚髓亏组（P<0.05）。治疗前两组患者在健侧内囊后肢及大脑脚外侧处FA值及ADC值无明显差异；肾虚髓亏组在患侧内囊后肢及大脑脚外侧处FA值及ADC值均低于非肾虚髓亏组（P<0.05）。相关性分析得出，两组患者患侧内囊后肢FA值与患者治疗前NIHSS评分呈负相关；两组患者患侧内囊后肢FA值与患者改良Barthel指数评分及简化Fugl-Meyer运动功能评分呈正相关；肾虚髓亏组患者患侧内囊后肢FA值与肾虚髓亏证中医证候评分呈负相关。结论 肾虚髓亏是急性缺血性脑卒中运动功能障碍的重要病机。研究结果显示，两组皮质脊髓束损伤程度与神经功能及运动功能损伤存在相关性，且肾虚髓亏组在皮质脊髓束的损伤程度方面与其中医证候评分呈负相关。     

基于罗森塔尔效应的护理干预对脑出血后遗症病人的心理状况和生活质量的影响

目的:探讨基于罗森塔尔效应的护理干预对脑出血后遗症病人的心理状况和生活质量的影响。方法:按照随机数字表法将2019年1月—2021年1月收治的98例脑出血后遗症病人分为对照组和观察组各49例,对照组实施常规护理,观察组在常规护理基础上给予基于罗森塔尔效应的护理干预。干预前后采用卒中量表(NIHSS)、功能独立性评定量表(FIM)、生活活动功能测量量表Barthel指数(BADL)评定两组病人康复情况,采用非精神病性焦虑量表(HADS-A)、非精神病性抑郁量表(HADS-D)、自我效能量表(GSES)评定两组病人心理状态,采用生活质量简明调查表(SF-36)评定两组病人生活质量。结果:观察组病人干预后NIHSS评分低于对照组,FIM和BADL评分高于对照组(P<0.05);观察组病人干预后HADS-A和HADS-D评分低于对照组,GSES评分高于对照组(P<0.05);观察组病人干预后生活质量各维度(躯体功能、躯体角色、肌体疼痛、总体健康、活力、社会功能、情绪角色、心理卫生)评分均明显高于对照组(P<0.05)。结论:基于罗森塔尔效应的护理干预应用于脑出血后遗症病人能有效促进病人康复,改善病人心理状态,提高病人生活质量。     

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.