运用五运六气探析2012年发病规律

2012年为壬辰年,壬辰年为木运太过之年,这一年的司天之气辰戌太阳寒水生太过之木,致使风气大行。其发病多累及肝、胆、脾、肺等脏腑,易患眩晕、胁痛、飧泄、痹症等病。     

转化医学是实现精准医学的必由之路——思考精准医学、循证医学及转化医学之间的协同关系

Since entering of 21st Century, two paradigm shifts have been achieved in medical science. The first is the birth of translational medicine, and the second is precision medicine. The translational medicine improves the collaborations among the basic scientist, clinician, academy and pharmaceutical enterprise, which pushes forward translations from basic scientific discovery to clinical application. However, focusing on regulatory approval as only goal and criteria, the effect of translational medicine practice was far from satisfactory, and effective rate of most products are less than 50% in real world. Besides failure to bring in true values to patients, some new translated products add extra burden to the society, and in this case, the major beneficiary is pharmaceutical companies. It is not surprised that the conclusions generated by clinical studies tends to be obscure, and sometimes, even misleading if the translational studies are based on inaccurate diagnostic criteria, arbitrated disease staging and evaluation system. It is time to reconsider the rational goal of translational medicine. Instead of targeting at regulatory approval only, we should redefine the disease based on its molecular driver and underline mechanism. A new classification of disease needs to be developed by building a knowledge network for biomedical Research. Through multidisciplinary colla borations, the precision medicine, i.e. precise prevention, precise diagnosis, precise staging and precise evaluation of disease is pursued. Therefore, precision medicine is the ultimate goal of translational medicine, evidence based medicine is the tool for practice of translational medicine, and translational medicine is the pathfinder for realization of precision medicine.     

Genes,stress and the heart

Effects of stress and its relationship to coronary morbidity and mortality are modified by genetic variation between people. We do not uniformly react to stress, nor do we create equally stressful living environments. The underlying causes of such individual variation are, in part, heritable. Two possible mechanisms through which genetic variation modulates the link between stress and coronary disease are cardiovascular stress reactivity and coronary-prone personality. Both possess characteristics of genetic markers: they are stable, familial, and heritable; they associate with the disease and are prospectively predictive of it. Twin-family data on cardiovascular stress reactivity and coronary-prone personality provide instructive illustrations of the interrelationship of genes, stress, and the heart.     

大数据时代无服务器计算在科技情报服务中的应用

无服务器计算和科技情报的有机结合可形成数据、服务、应用的生态闭环，有助于为未来科技情报工作的发展提供有力支撑。研究分析了云计算在当前科技情报服务中的弊端和基于无服务器计算的科技情报服务平台的优势，总结了科技情报服务的基本流程和无服务器计算在科技情报服务系统中的应用经验，结合大数据、无服务器计算等技术和科技情报服务方法论构建了基于无服务器计算的科技情报服务系统模型和架构，为今后构建无服务器科技情报服务系统的相关研究与实践提供新思路。     

Large-scale analysis of interindividual variability in single and paired-pulse TMS data

ObjectiveThis study brought together over 60 transcranial magnetic stimulation (TMS) researchers to create the largest known sample of individual participant single and paired-pulse TMS data to date, enabling a more comprehensive evaluation of factors driving response variability.MethodsAuthors of previously published studies were contacted and asked to share deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to variability in response to single and paired-pulse TMS data.Results687 healthy participant’s data were pooled across 35 studies. Target muscle, pulse waveform, neuronavigation use, and TMS machine significantly predicted an individual’s single-pulse TMS amplitude. Baseline motor evoked potential amplitude, motor cortex hemisphere, and motor threshold (MT) significantly predicted short-interval intracortical inhibition response. Baseline motor evoked potential amplitude, test stimulus intensity, interstimulus interval, and MT significantly predicted intracortical facilitation response. Age, hemisphere, and TMS machine significantly predicted MT.ConclusionsThis large-scale analysis has identified a number of factors influencing participants’ responses to single and paired-pulse TMS. We provide specific recommendations to minimise interindividual variability in single and paired-pulse TMS data.SignificanceThis study has used large-scale analyses to give clarity to factors driving variance in TMS data. We hope that this ongoing collaborative approach will increase standardisation of methods and thus the utility of single and paired-pulse TMS.     

提高医用化学理论大课质量的教学实践和探索

理论大课能直接反映教师的教学水平和学生的学习质量。为了提高医学院校医用基础化学大课的教学质量，教师采取一系列措施和方法，从备课、上课、课后答疑、辅导、考试等各个环节入手，进行教学实践和探索，以期学生的成绩和能力有明显提高，经过多年的实践和探索，收到了良好的效果，达到了教学目的。     

大数据背景下卫生人员信息素质培训平台的开发

阐述在大数据背景下卫生人员信息素质提高的重要性,提出开发卫生人员信息素质培训平台是对卫生人员进行信息素质培训的有效途径,介绍平台架构模型、功能模块和应用效果。     

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model

BackgroundGeneral medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients’ risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward.MethodsWe included consecutive patients (ages 18-100) admitted to medical wards at a single medical center (January 1, 2013-December 31, 2018). We constructed a machine learning model using patient characteristics, comorbidities, laboratory tests, and patients’ emergency department (ED) management. The model was trained on data from the years 2013 to 2017 and validated on data from the year 2018. The area under the curve (AUC) for mortality prediction was used as an outcome metric. Youden index was used to find an optimal sensitivity-specificity cutoff point.ResultsOf the 118,262 patients admitted to the medical ward, 6311 died (5.3%). The single variables with the highest AUCs were medications administered in the ED (AUC = 0.74), ED diagnosis (AUC = 0.74), and albumin (AUC = 0.73). The machine learning model yielded an AUC of 0.924 (95% confidence interval [CI]: 0.917-0.930). For Youden index, a sensitivity of 0.88 (95% CI: 0.86-0.89) and specificity of 0.83 (95% CI: 0.83–0.83) were observed. This corresponds to a false-positive rate of 1:5.9 and negative predictive value of 0.99.ConclusionA machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.     

Big Events in Greece and HIV Infection Among People Who Inject Drugs

Big Events are processes like macroeconomic transitions that have lowered social well-being in various settings in the past. Greece has been hit by the global crisis and experienced an HIV outbreak among people who inject drugs. Since the crisis began (2008), Greece has seen population displacement, inter-communal violence, cuts in governmental expenditures, and social movements. These may have affected normative regulation, networks, and behaviors. However, most pathways to risk remain unknown or unmeasured. We use what is known and unknown about the Greek HIV outbreak to suggest modifications in Big Events models and the need for additional research.