基于《素问》运气七篇探讨壬辰年慢性肝病的诊治规律

《素问》运气七篇大论是基于天人合一的整体观,以五运六气立说,重点揭示疾病的发生与中医运气学说的关系,提出了具有指导意义的病因病机理论、诊治理论及方药理论,建构了独具特色的中医运气理论体系,对临床辨证论治起着越来越重要的作用。文章在《素问》运气七篇大论的基础上对壬辰年(2012年)的运气变化进行论述,并结合慢性肝病的病机特点,探讨壬辰年慢性肝病的中医诊治规律,以期为2012年慢性肝病的中医药诊治及养生调护提供参考。     

大数据背景下的脑卒中疾病负担研究方法概述

脑卒中发病具有致残率高、死亡率高的特点,给社会和个人带来巨大的疾病负担。在全球卫生系统高度信息化的今天,各国纷纷搭建与完善公共卫生信息平台,通过平台中的公共卫生信息实现收集、整理、挖掘、分析、共享一体化,研究脑卒中的疾病负担,为卫生决策提供支撑。本研究通过对国内外文献资料进行检索复习,概述了我国脑卒中疾病负担的研究方法...     

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.     

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.     

Physical health-related quality of life among older adults with personality disorders

Objectives: Personality Disorders (PDs) are associated with a multitude of negative consequences. The negative PD effects on health can be even more burdensome for older adults given the physical and social functioning changes that occur with age; however, the majority of research examining the influence of PDs focuses on younger adults. The present study seeks to investigate the relationship between PDs and physical health-related quality of life (PHRQoL) in adults over the age of 50.

Methods: Data for 16,884 adults ages 50 and older from the 2001–2002 National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were analyzed. Multiple linear regression models were analyzed to investigate the relationships of seven PDs and participants’ PHRQoL.

Results: All PDs except histrionic and avoidant PD had statistically significant negative associations with PHRQoL scores, indicating that respondents diagnosed with PDs were expected to have lower PHRQoL than those without PDs, after controlling for sociodemographic characteristics. When psychosocial covariates were added to the model, only dependent, obsessive-compulsive and paranoid PDs were significantly related to PHRQoL score.

Conclusions: For adults ages 50 and older, a diagnosis of PD was weakly associated with lower PHRQoL scores for three PDs, however this is unlikely to be a causal association. The strength of the relationship between PDs and PHRQoL varies by type of PD. Given the higher rates of functional and social changes that occur with age, future research should focus on potential causes of worse physical health among older adults with PDs.     

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.     

聊城市城区部分近视和正常视力高中生人格特征及心理健康状况

目的:了解聊城市城区部分近视高中生的人格特征及心理健康状况,为有针对性地开展心理卫生保健,帮助学生及时解决心理及情绪问题提供参考。方法:2013年11月,在聊城市城区选取聊城一中和三中高中部分学生650名,根据其健康查体结果分为近视组(无其他疾病)学生365名和对照组(无眼部疾患且身体健康)学生285名。运用艾森克人格问卷和症状自评量表(SCL-90)进行调查。结果:近视组学生人格特征以情绪型性格占多数(53.48%),外向型性格次之(35.10%);对照组学生以外向型性格占多数(56.25%),情绪型性格次之(32.72%),两组学生人格特征差异有显著统计学意义(χ2=30.64,P0.01)。近视组SCL-90量表总均分、阳性均分及阳性项目数均值明显高于对照组(U=4.080~9.173,P0.001);两组学生SCL-90量表9个因子得分除躯体化症状外,其余各因子得分近视组均高于对照组(U=2.150~8.278,P0.0 5~0.001),以抑郁、焦虑、人际关系3项因子差别较为明显(U=5.146~8.278,P0.001),强迫症状、敌对、恐怖3项因子次之(U=2.679~3.032,P0 01);近视组SCL-90量表9个因子分≥3分的阳性人数也分别高于对照组,其中仅抑郁、焦虑两因子差异有显著统计学意义(χ2=7.575,P0.01;χ2=5.616,P0.05)。结论:近视组高中学生的人格特征明显没有对照组学生健康,其心理健康状况也比正常视力学生差,应根据近视学生的心理特点及个性特征开展心理卫生及健康指导,注意用眼卫生,疏导心理压力,提高学生心理健康素质。