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.     

A Predictive Model for Reactive Tube Feeding in Head and Neck Cancer Patients Undergoing Definitive (Chemo) Radiotherapy

AimsCareful management of a patient's nutritional status during and after treatment for head and neck squamous cell cancers (HNSCC) is crucial for optimal outcomes. The aim of this study was to develop a model for stratifying a patient's risk of requiring reactive enteral feeding through a nasogastric tube during radiotherapy for HNSCC, based on clinical and treatment-related factors.Materials and methodsA cohort of consecutive patients treated with definitive (chemo)radiotherapy for HNSCC between January 2016 and January 2018 was identified in the institutional electronic database for retrospective analysis. Patients requiring enteral feeding pretreatment were excluded. Clinical and treatment data were obtained from prospectively recorded electronic clinical notes and planning software.ResultsBaseline patient characteristics and tumour-related parameters were captured for 225 patients. Based on the results of the univariate analysis and using a stepwise backwards selection process, clinical and dosimetric variables were selected to optimise a clinically predictive multivariate model, fitted using logistic regression. The parameters found to affect the probability, P, of requiring a nasogastric feeding tube for >4 weeks in our clinical multivariate model were: tumour site, tumour stage (early T0/1/2 stage versus advanced T3/T4 stage), chemotherapy drug (none versus any drug) and mean dose to the contralateral parotid gland. A scoring model using the regression coefficients of the selected variables in the clinical multivariate model achieved an area under the curve (AUC) of 0.745 (95% confidence interval 0.678–0.812), indicating good discriminative performance. Internal validation of the model involved splitting the dataset 80:20 into training and test datasets 10 times and assessing differences in AUC of the model fitted to these.ConclusionsWe developed an easy-to-use prediction model based on both clinical and dosimetric parameters, which, once externally validated, can lead to more personalised treatment planning and inform clinical decision-making on the appropriateness of prophylactic versus reactive enteral feeding.     

青海地区获得性耐药结核病产生的危险因素及其预测模型的构建

目的探讨青海地区原发性与获得性耐药结核病的危险因素及临床发病风险预测模型构建。方法选取2018年7月-2019年7月青海地区132例耐药结核病患者为观察组,青海地区132例非耐药结核病患者为对照组,查阅患者的的临床资料,并自制调查问卷,以问卷调查的方式收集患者的相关信息,分析两组患者的临床指征,采用多因素Logistic回归分析完成风险模型建立,绘制ROC曲线分析风险模型的预测效能。结果单因素结果表明:青海地区原发性与获得性耐药结核病发生率与年龄、性别、与患者接触、3月底痰涂片结果无统计学意义(P>0.05);与居住地、婚姻状况、家庭收入、治疗末痰涂片结果、体重指数、抗结核药物性肝损害、结核病灶数、结核空洞、合并糖尿病及登记分类初治具有统计学意义(P<0.05);多因素logistic结果表明:青海地区原发性与获得性耐药结核病发生率与居住地、登记分类、体重指数、抗结核药物性肝损害、3月末肺结核病灶数及痰涂片结果,具有统计学意义(P<0.05);ROC曲线结果表明:构建获得性耐药结核病发病风险预测模型用于青海地区原发性与获得性耐药结核病患者中ACU值为0.847,预测敏感性为87.46%,特异性为90.29%。结论青海地区原发性与获得性耐药结核病危险因素较多,不同因素能相互作用、相互影响,构建获得性耐药结核病发病风险预测模型,能较好的预测临床发病,有助于指导临床诊疗。     

基于CT图像的评分模型预测食管鳞癌喉返神经旁淋巴结转移风险

目的:基于原发肿瘤及淋巴结CT特征建立评分模型预测食管鳞癌患者喉返神经旁淋巴结(RLN-LN)转移风险。方法:回顾性收集2014年1月至2019年12月于北京大学肿瘤医院行食管癌根治术并清扫RLN-LN的92例食管鳞癌患者。根据术后淋巴结病理结果分为RLN-LN转移组(n=37)和非转移组(n=55)。评估术前CT图像,记录食管癌患者年龄、性别、分化程度、肿瘤位置、肿瘤大小(肿瘤长度、肿瘤厚度、厚度/长度)、RLN-LN大小(淋巴结短径、长径、短径/多平面重建(MPR)最长径]。采用多元logistic回归筛选独立预测因子并建立评分模型,采用ROC曲线评估评分模型及独立预测因子诊断RLN-LN转移的效能,采用Z检验比较曲线下面积(AUC)的差异。应用Hosmer-Lemeshow检验和校准曲线评估模型拟合度。结果:肿瘤位置、肿瘤长度、RLN-LN短径、短径/MPR最长径是RLN-LN转移的独立预测因子,其诊断RLN-LN转移的AUC分别为0.586、0.705、0.831、0.777。基于以上4个CT特征建立评分模型,评分模型诊断RLN-LN转移的AUC为0.903(95%CI 0.846~0.959),优于各单一CT特征(Z=5.812,P<0.001;Z=2.161,P=0.030;Z=2.929,P=0.003;Z=4.052,P<0.001)。拟合优度Hosmer-Lemeshow检验结果显示P=0.555,校准曲线提示评分模型预测RLN-LN转移风险与实际转移风险之间具有良好的一致性。结论:基于CT图像的评分模型有助于食管鳞癌RLN-LN转移状态危险分层。