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

1. Download : Download high-res image (173KB)
2. Download : Download full-size image

     

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.     

基于移动医疗的妊娠期盆底肌训练对产后尿失禁发生的影响：一项基于随机对照试验设计的探索性研究

背景 孕产妇尿失禁（UI）发病率高，严重影响了女性生活质量。研究表明，盆底肌训练是UI有效的防治手段，本研究前期进行了一项随机对照试验（RCT），结果发现相比于常规宣教，基于移动医疗APP的盆底肌训练并未显现出预防优势，其原因需要进一步深入探讨。 目的 本研究拟对一项基于APP的妊娠期盆底肌训练的干预研究的阴性结果进行探索性分析，旨在探讨产后UI预防效果的影响因素以及获益的亚组人群。 方法 本研究数据来源于前期开展的一项RCT，采用方便抽样法，于2020年6—10月在南方医科大学深圳医院产科门诊招募了126例研究对象，采用随机数字表法分为干预组与对照组，每组各63例。对照组采用常规护理，干预组在此基础上使用"有爱屋"APP进行尿失禁自我管理，干预周期为2个月。产后42 d随访时收集两组产后相关资料，包括产后42 d UI发生情况。以产后是否发生UI为结局指标，将研究对象分为病例组和对照组，采用Logistic回归分析探讨混杂因素及其与干预方式之间的交互作用对产后UI发生的影响。针对Logistic回归分析的结果进行分层分析，探讨是否存在能从APP干预中获益的亚组人群。 结果 病例组和对照组阴道分娩史、入组时存在UI、Broome盆底肌自我效能量表（BPMSES）得分比较，差异均有统计学意义（P<0.05）。Logistic回归分析结果显示，入组时存在UI是产后发生UI的危险因素〔OR=15.897，95%CI（4.724，53.495），P<0.001〕；BPMSES得分与干预方式的交互作用可影响产后UI的发生〔OR=1.034，95%CI（1.017，1.051），P<0.001〕。分层分析结果显示，入组时存在UI症状的孕妇，干预组产后UI发生率低于对照组（χ2=4.18，P=0.041）；入组时不存在UI症状的孕妇，两组产后UI发生率比较，差异无统计学意义（χ2=1.89，P=0.284）。 结论 推荐有UI症状的孕妇使用"有爱屋"APP或许可预防产后UI的发生。而对于妊娠期没有UI症状的人群使用"有爱屋"APP预防产后UI发生的证据尚不充分。另外，不管有无UI症状，盆底肌训练自我效能高的孕妇有望从APP干预中获益。     

SARIMA模型在苏州市肺结核发病预测中的应用

目的 建立苏州市肺结核发病的SARIMA模型并预测发病,为苏州市肺结核防控提供参考。方法 收集结核病信息管理系统（新）中苏州市2010年1月—2018年12月肺结核月发病数,通过时间序列分析建立SARIMA模型并预测苏州市2019年肺结核的发病情况。结果 苏州市肺结核发病数具有明显的季节周期性,每年的发病最高峰为5月,发病最低谷为2月。苏州市肺结核发病数的最佳拟合模型为SARIMA (0,1,1)×(0,1,1)₁₂,AIC=9.590,SBC=9.644,模型参数均具有统计学意义,模型残差为白噪声序列,模型的预测值与实际值平均绝对百分比误差MAPE=7.943%,模型预测精度较高。预测苏州市2019年肺结核发病数为3 467例,月发病数平均值为289例,发病水平较2018年略有下降。结论 SARIMA(0,1,1)×(0,1,1)₁₂模型能较好拟合出苏州市肺结核发病数的时间变化趋势,可应用于苏州市肺结核月发病数的短期预测。