行为-结构化-关系干预模式对短期住院孤独症谱系障碍儿童的疗效分析

目的 探讨以行为-结构化-关系(BSR)干预模式对短期住院孤独症谱系障碍(ASD)儿童的干预疗效,为改善ASD儿童的预后提供参考依据。方法 选取2015年12月－2016年12月确诊为ASD的2~6岁儿童141例,随机分为治疗组和对照组。治疗组ASD儿童均接受BSR模式的短期课程训练,训练课程包括个别辅导、游戏课、运动课、音乐课等,每天训练时间6 h,持续1个月;对照组ASD儿童处于干预等待,接受随访观察和评估。所有ASD儿童干预前、后均接受儿童心理教育评估(第三版)(PEP-3)来进行各方面能力变化的评估。结果 治疗组、对照组干预前各副测验的原积分差异无统计学意义(P>0.05)。两组ASD儿童干预后认知、语言理解、模仿、情感表达、社会互动、行为特征-非语言、适应行为的原积分比较,差异均有统计学意义(t=2.41、2.02、4.14、3.69、4.42、2.69、2.96,P<0.05);但小肌肉、大肌肉、自理的原积分比较,差异无统计学意义(t=-1.13、-1.05、-0.84,P>0.05)。结论 BSR干预模式能够有效改善短期住院ASD儿童的预后,可推广用于儿童ASD的治疗。     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

前房注射卡波姆建立大鼠高眼压模型的观察

目的　前房注射卡波姆建立大鼠高眼压模型，观察卡波姆升眼压效果及对大鼠眼前节和视网膜的影响。方法　随机选取30只SD大鼠，注射前3 d早晚测量基线眼压。右眼定为实验眼，左眼定为对照眼，右眼放出房水后将30 μL的5 g·L^-1卡波姆混悬液注入前房，每日早10时、晚22时在大鼠清醒状态下测量眼压。每周进行双眼眼前节照相并对比。4周末处死26只大鼠（另4只持续观察眼压变化至注射后9周）并取双眼眼球行HE染色，观察实验眼与对照眼视网膜形态，对比视网膜厚度及房角形态。结果　注射前，实验眼白天和夜间眼压分别为（11.10±0.90）mmHg（1 kPa=7.5 mmHg）和（11.92±1.07）mmHg，对照眼分别为（11.22±1.07）mmHg和（11.76±1.08）mmHg；实验眼与对照眼相比，白天、夜间眼压差异均无统计学意义（均为 P＞0.05）；白天与夜间眼压相比，实验眼、对照眼差异均有统计学意义（均为P<0.05）。卡波姆在前房中呈现出弥散型和沉积型两种存在方式，弥散型和沉积型大鼠1周内眼压分别为（17.83±3.54）mmHg和（13.00±1.55）mmHg，两者相比差异具有统计学意义（P＜0.05）。注射后第1天至第19天，实验眼与对照眼白天眼压相比差异均具有统计学意义（均为P＜0.05）；注射后第1天至第27天，实验眼与对照眼夜间眼压相比差异均具有统计学意义（均为P＜0.05）。实验眼视网膜形态发生改变，注射后4周视网膜厚度为（254.70±21.80）μm，与对照眼的（346.73±24.63）μm相比，差异有统计学意义（P=0.00）。实验眼前房充满卡波姆及虹膜的混合成分，紧贴角膜内皮并延伸至房角，堵塞小梁网结构，正常虹膜形态消失；对照眼房角形态正常。结论　前房注射卡波姆建立大鼠高眼压模型，可维持高眼压4周以上，昼夜眼压差异较为明显，夜间眼压较白天更高，4周后视网膜出现高眼压损伤后的表现。     

Outcomes of coronary artery bypass grafting in patients with heart failure with a midrange ejection fraction

BackgroundCoronary artery bypass grafting (CABG) improves survival in patients with heart failure and severely reduced left ventricular systolic function (LVEF). Limited data exist regarding adverse cardiovascular event rates after CABG in patients with heart failure with midrange ejection fraction (HFmrEF; LVEF > 40% and < 55%).MethodsWe analyzed data on isolated CABG patients from the Veterans Affairs national database (2010-2019). We stratified patients into control (normal LVEF and no heart failure), HFmrEF, and heart failure with reduced LVEF (HFrEF) groups. We compared all-cause mortality and heart failure hospitalization rates between groups with a Cox model and recurrent events analysis, respectively.ResultsIn 6533 veterans, HFmrEF and HFrEF was present in 1715 (26.3%) and 566 (8.6%) respectively; the control group had 4252 (65.1%) patients. HFrEF patients were more likely to have diabetes mellitus (59%), insulin therapy (36%), and previous myocardial infarction (31%). Anemia was more prevalent in patients with HFrEF (49%) as was a lower serum albumin (mean, 3.6 mg/dL). Compared with the control group, a higher risk of death was observed in the HFmrEF (hazard ratio [HR], 1.3 [1.2-1.5)] and HFrEF (HR, 1.5 [1.2-1.7]) groups. HFmrEF patients had the higher risk of myocardial infarction (subdistribution HR, 1.2 [1-1.6]; P = .04). Risk of heart failure hospitalization was higher in patients with HFmrEF (HR, 4.1 [3.5-4.7]) and patients with HFrEF (HR, 7.2 [6.2-8.5]).ConclusionsHeart failure with midrange ejection fraction negatively affects survival after CABG. These patients also experience higher rates myocardial infarction and heart failure hospitalization.     

“人工智能+X”背景下医学信息管理专业研究生培养模式

阐述当前“人工智能+X”背景下市场对医学信息管理专业人才能力的需求，分析医学信息管理专业人才培养现状，提出从重塑学科人才培养目标、优化课程内容与课程设置、建设“双师型”导师队伍、搭建多方协同共建共享在线平台及设立“政用产学研”联合培养基地等方面探索医学信息管理专业研究生培养模式，以期培养适应人工智能时代发展，具备学科优势特色的高层次、高水平、高质量的复合型、应用型、创新型人才。     

数据之美——聚焦全球器官捐献发展趋势

器官移植术是20世纪出现的针对器官功能衰竭的最有效治疗方法，每年拯救全球超过12万例患者。但供器官短缺的现状，与器官移植技术和辅助药物的发展不匹配，制约了器官移植事业的发展。我国自2015年起已成为全球器官捐献和移植大国之一，2017年公民逝世后器官捐献数量超过5 000例，占全球捐献总量的15%以上。黄洁夫教授总结的器官捐献与移植"中国模式"得到了世界卫生组织、国际移植界的高度重视和充分肯定。本文通过整理全球及各国的器官捐献与移植数据，剖析全球现状与发展趋势，进一步探索我国公民器官捐献的影响因素并提出针对性的应对策略，以期实现我国器官捐献和移植的"自给自足"。