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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郑州地区孕妇铁营养状况及其影响因素调查

目的 探讨郑州地区孕妇铁营养状况及其影响因素。方法 以2021年4—8月在郑州市某三家医院建立健康档案并进行孕期体检的孕妇作为研究对象进行问卷调查及铁营养状况检测,采用描述流行病学方法对孕妇铁营养状态进行分析,并采用单、多因素分析方法对孕妇铁缺乏状况影响因素进行分析。结果 共有3 258例孕妇参与本次研究,其中妊娠早期989例,占30.36%,妊娠中期1 125例,占34.53%,妊娠晚期1 144例,占35.11%。在3 258名孕妇中,共有773例为铁缺乏,铁缺乏的检出率为23.73%。多因素分析结果显示,年龄≥35岁（OR=2.726）、处于妊娠的中期或晚期（OR=3.037,3.438）、月经初潮年龄<13岁（OR=2.432）、经产妇（OR=3.493）、流产次数≥3次（OR=2.648）、孕前月经量≥100 mL/次（OR=2.420）、文化程度为初中及以上（OR=0.517,0.459）、居住在农村（OR=3.684）、家庭月收入>3 000元及以上（OR=0.601,0.542）、居住房屋装修时间<1年（OR=6.449）、罹患消化系统疾病（OR=4.716）、有营养补齐剂摄入（OR=0.355）是孕妇铁缺乏的影响因素。结论 郑州市孕妇铁缺乏检出率较高,需要尽早给予针对性的干预。部分因素被确定为该地区孕妇人群发生铁元素缺乏的影响因素,可选择性的对其中危险因素给予干预。     

A High Order Bound Preserving Finite Difference Linear Scheme for Incompressible Flows

We propose a high order finite difference linear scheme combined with a high order bound preserving maximum-principle-preserving (MPP) flux limiter to solve the incompressible flow system. For such problem with highly oscillatory structure but not strong shocks, our approach seems to be less dissipative and much less costly than a WENO type scheme, and has high resolution due to a Hermite reconstruction. Spurious numerical oscillations can be controlled by the weak MPP flux limiter. Numerical tests are performed for the Vlasov-Poisson system, the 2D guiding-center model and the incompressible Euler system. The comparison between the linear and WENO type schemes, with and without the MPP flux limiter, will demonstrate the good performance of our proposed approach.     

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.     

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。