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.     

Contribution of different somatosensory afferent input to subcortical somatosensory evoked potentials in humans

ObjectivesTo investigate the subcortical somatosensory evoked potentials (SEPs) to electrical stimulation of either muscle or cutaneous afferents.MethodsSEPs were recorded in 6 patients suffering from Parkinson’s disease (PD) who underwent electrode implantation in the pedunculopontine (PPTg) nucleus area. We compared SEPs recorded from the scalp and from the intracranial electrode contacts to electrical stimuli applied to: 1) median nerve at the wrist, 2) abductor pollicis brevis motor point, and 3) distal phalanx of the thumb. Also the high-frequency oscillations (HFOs) were analysed.ResultsAfter median nerve and pure cutaneous (distant phalanx of the thumb) stimulation, a P1-N1 complex was recorded by the intracranial lead, while the scalp electrodes recorded the short-latency far-field responses (P14 and N18). On the contrary, motor point stimulation did not evoke any low-frequency component in the PPTg traces, nor the N18 potential on the scalp. HFOs were recorded to stimulation of all modalities by the PPTg electrode contacts.ConclusionsStimulus processing within the cuneate nucleus depends on modality, since only the cutaneous input activates the complex intranuclear network possibly generating the scalp N18 potential.SignificanceOur results shed light on the subcortical processing of the somatosensory input of different modalities.     

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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Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

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.     

动物瘢痕模型的相关研究进展

皮肤损伤后的异常愈合会导致病理性瘢痕的产生。病理性瘢痕的出现不仅影响美观,严重时还会造成心理和生理功能障碍。病理性瘢痕的机制研究对于瘢痕治疗有极为重要的意义。其中,动物瘢痕模型是目前研究病理性瘢痕的重要模型手段之一。理想的动物瘢痕模型应该在组织学、细胞学等层面尽可能接近于人类的病理性瘢痕。该文分别从传统技术动物瘢痕中的啮齿类动物模型、兔耳模型和猪模型,以及新技术动物瘢痕模型这两个方面,结合近年来在瘢痕领域应用较多的研究,进行了相应系统的阐述。