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.     

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.     

Precision and Accuracy of Measurements on CT Scout View

ObjectivesThe purposes of this study were to (1) investigate the limits of measurements on scout view in three computed tomography axes, x, y and z and (2) develop a model to provide better understanding of measurement accuracy.MethodsFor the first objective, anteroposterior and lateral scout views of a Catphan phantom 200 mm in diameter and length were acquired with a GE scanner at 21 different table heights. Phantom measurements on scout view were performed by two experienced readers. The comparison of their measures provided estimation of precision. The accuracy was assessed by determining the bias, calculated as the difference between the values measured on scout view and the real phantom size. Second, a model was developed investigating the relationship between the dimensions of the object, its image, and the table height. This relationship was tested on our data.ResultsScout view measurements were precise, with less than 0.53% difference between readers. In addition, small biases of about 1 mm were detected in the z-axis, whatever the table height. In the other axes, serious biases from −13 to +73 mm were measured. Furthermore, at isocentre, overestimations up to 7 mm were shown. The results also indicated that biases in scout view measurements are because of the geometrical projection related to the object-detector distance.ConclusionsMeasurements in the table movement axis are precise and accurate, conferring to scout views an added value for preoperative planning in orthopedic surgery.     

颞下颌关节强直动物模型中最大张口度与强直严重程度关系研究

目的　研究主、被动最大张口度（AMMO、PMMO）与颞下颌关节强直严重程度的关系。方法　选取28只健康雄性绵羊随机分为实验组和对照组，每组各14只。实验组绵羊双侧颞下颌关节模拟髁突矢状骨折，其中左侧翼外肌被切断以阻断其功能；对照组绵羊未进行手术。于术前及术后12、24周对所有绵羊体重、AMMO、PMMO、颞下颌关节形态学特点进行测量评估。结果　实验组只有右侧保留翼外肌功能的颞下颌关节发生了骨强直。术后12、24周，实验组绵羊AMMO、PMMO、极限距离均显著低于对照组，差异均有统计学意义（均P < 0.05）。实验组绵羊AMMO和PMMO与骨融合区宽度、长度、面积及钙化程度均呈负相关（均P < 0.05），其中骨融合区面积为主要影响因素（术后12、24周相关系数r分别为-0.94、-0.95）。结论　颞下颌关节强直动物模型中阻断翼外肌功能可阻止骨强直的发生；对于早期髁突矢状骨折，可通过牙合垫或牙合板进行张口训练，进而阻断翼外肌功能。当颞下颌关节发生骨强直时，骨融合区面积越大，张口受限越明显。