基于标准化患者法评价四川农村基层医生两种慢性病诊断准确性及其影响因素

目的 运用标准化患者法评估四川农村地区基层医生不稳定型心绞痛和2型糖尿病两种慢性病诊断准确性现状,探讨基层医生两种慢性病诊断准确性的主要影响因素,为提升基层医生两种慢性病诊断准确性提供科学依据。方法 采用多阶段随机整群抽样方法,抽取四川省自贡市5个区/县50个乡镇100个村为研究现场,以调查当日在岗的全科及内科医生作为研究对象。共进行两轮数据采集,第1轮采集样本乡镇卫生院和村卫生室医生的基本信息;第1轮调查完成1个月后,运用标准化患者法开展第2轮调查,收集农村基层医生对不稳定型心绞痛和2型糖尿病诊断结果信息。运用Logistic回归分析农村基层医生不稳定型心绞痛和2型糖尿病诊断准确性的影响因素。结果 共纳入172名农村基层医生,完成186次标准化患者访问,正确诊断率为48.39%。其中不稳定型心绞痛的正确诊断率为18.68%(17/91),2型糖尿病的正确诊断率为76.84%(73/95)。Logistic回归分析显示,具有执业医师资质的农村基层医生更有可能做出正确诊断(OR=4.857,95%CI=1.076~21.933,P=0.040)。农村基层医生在诊断过程中涉及的必要问诊和检查条目越多,做出正确诊断的概率越高(OR=1.627,95%CI=1.065~2.485,P=0.024)。与不稳定型心绞痛相比,农村基层医生对2型糖尿病做出正确诊断的可能性更高(OR=6.306,95%CI=3.611~11.013,P<0.001)。结论 四川农村基层医生不稳定型心绞痛和2型糖尿病诊断准确性整体较差,建议以基层医生慢性病诊断过程质量改善为突破口,提升基层医生执业水平,进而提高慢性病诊断准确性。     

基于网络药理学和分子对接技术探讨黄芪干预腹膜纤维化的机制

目的 运用网络药理学方法及分子对接技术探讨黄芪干预腹膜纤维化的可能机制。方法 利用中药系统药理学数据库及分析平台(TCMSP)检索黄芪的主要化学成分及靶点,并补充文献报道相关药理作用的成分作为潜在活性成分。以"peritoneal fibrosis"为关键词分别在OMIM、Genecards获取目前已知的与腹膜纤维化相关的疾病靶点,后取两者的交集靶点;对交集基因通过STRING数据库与Cytoscape 3.7.2软件构建"药物-成分-靶点-疾病"网络及蛋白互作(PPI)网络并筛选核心网络。基于R软件使用Bioconductor生物信息软件对核心靶点进行GO及KEGG富集分析,最终采用AutoDock软件将主要有效成分与核心靶点进行分子对接,得出其结合能力。结果 筛选出20个黄芪活性成分及文献报道有相关药理作用4个, 457药物作用靶点,与674个腹膜纤维化病靶点取交集,得到86个共同靶点。GO功能富集分析提示黄芪拮抗腹膜纤维化主要参与了蛋白激酶B信号转导的调节、细胞对化学的应激反应、炎症反应的调节等通路; KEGG通路富集分析主要涉及调控肿瘤、磷脂酰肌醇-3-羟激酶-蛋白激酶B(PI3K-Akt)、晚期糖基化终末产物/晚期糖基化终末产物受体(AGE-RAGE)、人类巨细胞病毒感染、HIF-1信号通路等;分子对接结果显示关键靶点与活性成分具有较好的结合能力。结论 黄芪治疗腹膜纤维化的分子机制,可能与抑制炎症及氧化应激反应、调节多种信号通路等相关。     

中药生产过程质量控制关键技术研究进展

中医药发展已上升到国家战略层面,在医药行业贯彻实施"中国制造2025"战略的新形势下,中药生产过程质量控制是中药工业需要加快突破的关键领域之一。对中药生产过程质量控制领域在工艺设计、分析检测、过程建模、制造装备等方面的关键共性问题进行解析,综述了中药生产过程质量控制体系中工艺过程理解、生产过程实时分析方法开发、过程控制策略建立3个方面的研究进展;并结合企业研究实践,介绍了质量源于设计(quality by design,Qb D)、过程分析技术(process analytical technology,PAT)、实验设计(design of experiment,DOE)、多变量统计分析等关键技术在上述3个研究方向中的应用进展,分析了实际工业应用的难点问题并对其应用前景进行展望,旨在为中药企业应用和提升生产过程质量控制技术提供参考。     

基于雨课堂的临床流行病学教学改革与实践

目的探讨雨课堂在临床流行病学教学中的作用和效果。方法采用随机对照法在本科阶段无流行病学学习经历的临床专业硕士研究生中,使用雨课堂线上课前预习及课后随堂练习的功能,用于评价雨课堂线上功能的辅助教学效果。结果应用雨课堂线上功能干预组与对照组在基线、阶段小测和末考卷面成绩均无统计学差异;但对于主动学习,雨课堂参与度较高的学生成绩提高明显。结论雨课堂有利于理论性较强课程的学习,但是仍不能忽略课堂教学师生互动过程。     

Clinical Diagnostic Implications of Body Fluid MiRNA in Oral Squamous Cell Carcinoma: A Meta-Analysis

Oral cancer, predominantly oral squamous cell carcinoma (OSCC), is one of the most leading causes of cancers worldwide. Due to a low 5-year survival rate, highly effective methods for the early detection of OSCC are totally needed. MicroRNAs (miRNAs), as promising biomarkers, can bring insights into tumorigenesis of oral cancers. However, studies on the accuracy of miRNAs detection in OSCC have inconsistent conclusions, leading us to conduct this meta-analysis. The aim of this study was to systematically review the articles investigating the diagnostic value of miRNAs in OSCC.The PubMed, Embase, Chinese National Knowledge Infrastructure (CNKI), Web of Science were searched (updated to June 11th, 2015) to identify all articles evaluating the diagnostic yield of miRNAs for OSCC. The pooled sensitivity, specificity, and other diagnostic parameters were used to assess the performance of miRNAs assays on OSCC detection. Statistical analysis was conducted by employing the R software.The present meta-analysis comprised 23 studies from 10 articles, including 598 OSCC patients and 320 healthy individuals, available for analysis. The summary receiver operator characteristic (SROC) curve was plotted. Meanwhile, the pooled diagnostic parameters and the area under curve (AUC) were calculated based on all included studies. The pooled diagnostic parameters calculated from all 23 studies were as follows: pooled sensitivity of 0.759 (95% CI: 0.701–0.809), pooled specificity of 0.773 (95% CI: 0.713–0.823) and AUC of 0.832, which indicates a relatively high diagnostic accuracy of miRNAs in differentiating OSCC patients from healthy controls. Meanwhile, In addition, subgroup analyses were conducted to access the heterogeneity between studies, which is based on specimen (serum/plasma/blood/saliva/ tissue) and ethnicity (Asian/Caucasian).In summary, our meta-analysis suggests that miRNAs might be used in noninvasive screening tests for OSCC, which needs further large-scale studies to be validated.