A case of sudden cardiac death due to mitochondrial disease

A 25-year-old, emaciated man without medical treatment was found to have died suddenly at home by his mother. At autopsy, there were no injuries to his body, but significant circulatory insufficiency was observed. Electron microscopy revealed abnormal mitochondria in cells of the cardiac conduction system. The conduction system was filled with mitochondrial size abnormalities and mitochondrial cristae abnormalities. No notable abnormal findings were observed in other organs. Genetic examination of the blood revealed the mitochondrial pathogenetic variant m.3243A>G. Epileptic seizures, diabetic ketoacidosis, and hyperosmolar hyperglycemic state were unlikely to be the cause of sudden death. The cause of death was diagnosed as arrhythmia possibly induced by the failure of the cardiac conduction system due to mitochondrial disease. This is a rare case of sudden death caused by an accumulation of abnormal mitochondria in the cardiac conduction system.     

Hepatitis B and circadian rhythm of the liver

The circadian rhythm in humans is determined by the central clock located in the hypothalamus’s suprachiasmatic nucleus, and it synchronizes the peripheral clocks in other tissues. Circadian clock genes and clock-controlled genes exist in almost all cell types. They have an essential role in many physiological processes, including lipid metabolism in the liver, regulation of the immune system, and the severity of infections. In addition, circadian rhythm genes can stimulate the immune response of host cells to virus infection. Hepatitis B virus (HBV) infection is the leading cause of liver disease and liver cancer globally. HBV infection depends on the host cell, and hepatocyte circadian rhythm genes are associated with HBV replication, survival, and spread. The core circadian rhythm proteins, REV-ERB and brain and muscle ARNTL-like protein 1, have a crucial role in HBV replication in hepatocytes. In addition to influencing the virus’s life cycle, the circadian rhythm also affects the pharmacokinetics and efficacy of antiviral vaccines. Therefore, it is vital to apply antiviral therapy at the appropriate time of day to reduce toxicity and improve the effectiveness of antiviral treatment. For these reasons, understanding the role of the circadian rhythm in the regulation of HBV infection and host responses to the virus provides us with a new perspective of the interplay of the circadian rhythm and anti-HBV therapy. Therefore, this review emphasizes the importance of the circadian rhythm in HBV infection and the optimization of antiviral treatment based on the circadian rhythm-dependent immune response.     

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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PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

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。