一种预测子宫内膜病变患者内膜病理类型的临床模型＊

目的 开发并评估一种用于预测子宫内膜病变患者内膜病理类型的临床模型；方法 选取2019年11月至2021年11月因妇科B超发现子宫内膜病变并于山东中医药大学附属医院行宫腔镜下内膜活检的患者，结合其病史与最小绝对收缩和选择算子法（Least absolute shrinkage and selection operator，LASSO）筛选影响内膜病变的独立危险因素，列线图（nomogram）函数建立列线图模型，采用ROC曲线下方的面积大小（Area Under Curve， AUC）、C指数（C-index）、拟合优度（Hosmer-Lemeshow）检验、自举法（bootstrap）评估及验证模型，依据列线图对纳入患者进行风险赋分，绘制ROC曲线获取风险评分的截断值，从而划分高低风险的人群；结果 阴道流血、绝经、无流产史、并发高血压、B超内膜厚度增厚、内膜回声不正常，中医证型为虚实夹杂证是发生子宫内膜癌及癌前病变的独立危险因素，评估模型所得AUC值与C-index均>0.9，Hosmer-Lemeshow检验显示P>0.05，bootstrap内部验证法所得C-index亦高于0.9，均说明模型准确度、区分度及可信度良好，当患者的风险评分总和≥177分时，属于高风险人群；结论 本模型可以预测B超提示子宫内膜病变患者的内膜病理类型为癌前或恶变型的概率并识别高风险人群。     

Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma

ObjectiveTo evaluate the performance of a deep learning (DL)-based radiomics strategy on contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) status and clinical outcomes, recurrence-free survival (RFS) and overall survival (OS) in patients with early stage hepatocellular carcinoma (HCC) receiving surgical resection.MethodsAll 283 eligible patients were included retrospectively between January 2008 and December 2015, and assigned into the training cohort (n = 198) and the testing cohort (n = 85). We extracted radiomics features via handcrafted radiomics analysis manually and DL analysis of pretrained convolutional neural networks via transfer learning automatically. Support vector machine was adopted as the classifier. A clinical-radiological model for MVI status integrated significant clinical features and the radiological signature generated from the radiological model with the optimal area under the receiver operating characteristics curve (AUC) in the testing cohort. Otherwise, DL-based prognostic models were constructed in prediction of recurrence and mortality via Cox proportional hazard analysis.ResultsThe clinical-radiological model for MVI represented an AUC of 0.909, accuracy of 96.47%, sensitivity of 90.91%, specificity of 97.30%, positive predictive value of 83.33%, and negative predictive value of 98.63% in the testing cohort. The clinical-radiological models for identification of RFS and OS outperformed prediction performance of the clinical model or the DL signature alone. The DL-based integrated model for prognostication showed great predictive value with significant classification and discrimination abilities after validation.ConclusionsThe integrated DL-based radiomics models achieved accurate preoperative prediction of MVI status, and might facilitate predicting tumor recurrence and mortality in order to optimize clinical decisions for patients with early stage HCC.     

Circulating angiotensin converting enzyme 2 and COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered a widespread outbreak since December 2019. The SARS-CoV-2 infection-related illness has been dubbed the coronavirus disease 2019 (COVID-19) by the World Health Organization. Asymptomatic and subclinical infections, a severe hyper-inflammatory state, and mortality are all examples of clinical signs. After attaching to the angiotensin converting enzyme 2 (ACE2) receptor, the SARS-CoV-2 virus can enter cells through membrane fusion and endocytosis. In addition to enabling viruses to cling to target cells, the connection between the spike protein (S-protein) of SARS-CoV-2 and ACE2 may potentially impair the functionality of ACE2. Blood pressure is controlled by ACE2, which catalyzes the hydrolysis of the active vasoconstrictor octapeptide angiotensin (Ang) II to the heptapeptide Ang-(1-7) and free L-Phe. Additionally, Ang I can be broken down by ACE2 into Ang-(1-9) and metabolized into Ang-(1-7). Numerous studies have demonstrated that circulating ACE2 (cACE2) and Ang-(1-7) have the ability to restore myocardial damage in a variety of cardiovascular diseases and have anti-inflammatory, antioxidant, anti-apoptotic, and anti-cardiomyocyte fibrosis actions. There have been some suggestions for raising ACE2 expression in COVID-19 patients, which might be used as a target for the creation of novel treatment therapies. With regard to this, SARS-CoV-2 is neutralized by soluble recombinant human ACE2 (hrsACE2), which binds the viral S-protein and reduces damage to a variety of organs, including the heart, kidneys, and lungs, by lowering Ang II concentrations and enhancing conversion to Ang-(1-7). This review aims to investigate how the presence of SARS-CoV-2 and cACE2 are related. Additionally, there will be discussion of a number of potential therapeutic approaches to tip the ACE/ACE-2 balance in favor of the ACE-2/Ang-(1-7) axis.     

基于临床观察文本解析痛经辨治规律研究

目的:研究痛经的中医药辨治规律。方法:检索国家知识基础设施数据库(CNKI)1988—2019年应用中药治疗痛经的相关文献,采用Medcase Ver38诊籍中医师工作室-名老中医经验传承辅助平台,运用文本解析对临床医案进行研究。结果:研究文献131篇,医案104则,涉及病例128次,332诊次,涉及症状92条,病机33条,舌象27条,脉象17条,药物249种。结论:中医药治疗痛经的特异症状为“经行腹痛”,可见症为“夹血块、月经色暗、手足冰冷、经期腰酸”,相关舌脉为“舌质暗红,苔薄白”“脉沉细弦”,核心病机为“瘀血阻滞”,常用治法为“行气化瘀,活血止痛”,常用药物为“当归、白芍、甘草、延胡索、川芎”。     

Development of an evidence-based ESCALATION system for recognition and response to paediatric clinical deterioration

AimThe aim of this study was to develop an evidence-based paediatric early warning system for infants and children that takes into consideration a variety of paediatric healthcare contexts and addresses barriers to escalation of care.MethodsA three-stage intervention development framework consisted of Stage 1: evidence review, benchmarking, stakeholder (health professionals, decision-makers, and health consumers) engagement, and consultation; Stage 2: planning and coproduction by the researchers and stakeholders using action research cycles; and Stage 3: prototyping and testing.ResultsA prototype evidence-based system incorporated human factor principles, used a structured approach to patient assessment, promoted situational awareness, and included family as well as clinician concern. Family involvement in detecting changes in their child's condition was supported by posters and flyers codesigned with health consumers. Five age-specific observation and response charts included 10 weighted variables and one unweighted variable (temperature) to convey a composite early warning score. The escalation pathway was supported by a targeted communication framework (iSoBAR NOW).ConclusionThe development process resulted in an agreed uniform ESCALATION system incorporating a whole-system approach to promote critical thinking, situational awareness for the early recognition of paediatric clinical deterioration as well as timely and effective escalation of care. Incorporating family involvement was a novel component of the system.     

Updates in therapeutic drug monitoring in inflammatory bowel disease

Biologics and immunomodulators (IMM) are generally considered the most effective therapies for the treatment of ulcerative colitis and Crohn’s disease. However, despite the efficacy of these therapies, many patients either have a primary lack of response or a secondary loss of response to these medications. Therapeutic drug monitoring (TDM) is a systematic approach to managing such patients. In this review, we summarize the latest data on TDM, including reactive and proactive TDM, in patients with inflammatory bowel disease on biologics and/or IMM.     

区（县）级医院实用型科技创新人才培养路径分析：基于临床试验违规问题

背景 区（县）级医院是基层卫生服务的牵头医院，负责当地医疗及其教学、培训、研究工作。科技创新能力不足是影响其高质量发展的"瓶颈"。 目的 总结近10年文献中出现的临床试验违规问题，以此为基础探讨区（县）级医院培养实用新型科技创新人才的路径。 方法 于2022年1月，以"临床试验" "医学伦理" "违规问题"为中文关键词，"Clinical Trial" "Ethical Issues" "Clinical Trial" "Ethical Problems"为英文关键词，分别检索中国知网、万方数据知识服务平台、维普网、百度文库，以及PubMed、Medline、the Cochrane Library、EmBase、ClinicalKey数据库，获取发表时间为2010-01-01至2021-12-31的关于临床试验违规问题的相关文献。从文献中提取临床试验伦理违规问题并依据问题性质进行归类。采用系统聚类方法依据组间连接对违规问题进行聚类分析。采用根因分析确定主要问题的根本原因，并找出相应对策。 结果 共纳入71篇文献，提取出125个临床试验伦理违规问题。125个问题依据相互之间关联度归类整理得出27小类，5个类别问题，其中排在前三位的类别问题为受试者保护问题（42个，33.6%）、临床试验方案问题（33个，26.4%）及知情同意方面的问题（31个，24.8%）。聚类分析结果显示，125个问题可聚类为真实性（造假）与合规性（违规）两大类问题。根因分析结果显示：导致造假、违规问题的根本原因是研究者科研能力不足，伦理知识欠缺；其次是对造假行为的惩处力度不够、科技创新成果评价及伦理审查体系不完善的监管问题。 结论 未来解决造假与违规问题，可采取医教协同开展科技创新伦理思政实践教育的方法。