Biomechanical behavior of brain injury caused by sticks using finite element model and Hybrid-III testing

Objective: To study the biomechanical mechanism of head injuries beaten with sticks, which is common in the battery or assaultive cases. Methods: In this study, the Hybrid-III anthropomorphic test device and finite element model (FEM) of the total human model for safety (THUMS) head were used to determine the biomechanical response of head while being beaten with different sticks. Total eight Hybrid-III tests and four finite element simulations were conducted. The contact force, resultant acceleration of head center of gravity, intracranial pressure and von Mises stress were calculated to determine the different biomechanical behavior of head with beaten by different sticks. Results: In Hybrid-III tests, the stick in each group demonstrated the similar kinematic behavior under the same loading condition. The peak values of the resultant acceleration for thick iron stick group, thin iron stick group, thick wooden stick group and thin wooden stick group were 203.4 g, 221.1 g, 170.5 g and 122.2 g respectively. In finite element simulations, positive intracranial pressure was initially observed in the frontal comparing with negative intracranial pressure in the contra-coup site. Subsequently the intracranial pressure in the coup site was decreasing toward negative value while the contra-coup intracranial pressure increasing toward positive values. Conclusions: The results illustrated that the stiffer and larger the stick was, the higher the von Mises stress, contact force and intracranial pressure were. We believed that the results in the Hybrid-III tests and THUMS head simulations for brain injury beaten with sticks could be reliable and useful for better understanding the injury mechanism.     

基于医院信息系统的乳腺癌临床特征及中西医用药特征分析

目的 解析真实世界中乳腺恶性肿瘤患者的人群特征、诊断特征、中西医用药特征，为乳腺癌的临床防治提供参考。方法 采集2002年2月至2015年5月全国60家三级甲等医 院信息系统（Hospital Information System，HIS）中，出院诊断为“乳腺癌”的患者用药信息，采用SAS9.3统计软件，对人口学信息、诊断信息、医嘱用药信息等进行描述性分析。结果 39798例乳腺癌患者，平均年龄（50.93者，平均年龄）岁；多以门诊入院，入院病情以“一般”为主;合并疾病主要为高血压，骨肿瘤，联用西药以抑制肿瘤细胞增殖、治疗并发症、缓解放化疗不良反应为主；中医辨证以痰瘀互结证，气阴两虚证，肝气淤滞证，脾气亏虚证型最为常见，临床清热解毒剂、益气扶正剂，活血化瘀剂应用较多。结论 乳腺癌中西医结合治疗，联用药物广泛，临床治疗基本符合临床指南。     

一起学校耐药肺结核聚集性疫情的调查

目的 对北京市朝阳区某学校一起耐药肺结核疫情进行分析,为今后耐药结核病疫情的处置提供参考。方法 对病例进行流行病学调查,对病例密切接触者采用PPD试验、X线胸片和CT相结合的方式筛查。结果 2018年10月—2019年6月,该校共发生36例肺结核病例,发病率为4.5%。其中5例耐多药,3例耐利福平。36例病例分布在四个班,15a班27例、15b班4例、15c班2例、17d班3例。各班发病率分别为56.3%、8.3%、5.7%、7.1%,差异有统计学意义(P<0.01)。8例耐药病例中6例为15a班学生,占耐药病例总数的75.0%。经CT筛查68名密切接触者中确诊23例肺结核患者,检出率33.8%。结论 该起学校聚集性疫情为全国首起耐药肺结核聚集性疫情,首发病例未及时就医,传染源隐匿存在时间长,是导致该起疫情发生的主要原因。疫情处置中开展密切接触者筛查对于及时发现新病例非常重要。     

探讨替米沙坦对冠心病合并糖尿病肾病患者疗效的影响

目的:探究替米沙坦对冠心病合并糖尿病肾病患者疗效的影响情况。方法:56例探究目标对象均为某院接收的冠心病合并糖尿病肾病患者,挑选时间2018年6月~2019年6月。将"计算机表法"作为分组的参考,分配为参照组(n=28例)执行依那普利治疗,探究组(n=28例)执行替米沙坦治疗。结果:探究组的LVEF、LVEDd、肌酐、24h尿蛋白4项指标与参照组相比,差异有统计学意义(P<0.05);收缩压、舒张压、空腹血糖、餐后2h血糖4项指标与参照组相比,差异没有统计学意义(P>0.05)。结论:冠心病合并糖尿病肾病患者选择替米沙坦治疗后,心室功能的重构以及肾脏预后结局均得到改善,且临床效果比依那普利好,值得借鉴。     

Hypertension is a risk factor for adverse outcomes in patients with coronavirus disease 2019: a cohort study

Abstract

Background

Comorbidities are commonly seen in patients with coronavirus disease 2019 (COVID-19), but the clinical implication is not yet well-delineated. We aim to characterize the prevalence and clinical implications of comorbidities in patients with COVID-19.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.