The impact of different step rate threshold methods on physical activity intensity in older adults

BackgroundOlder adults benefit most from engaging in higher-intensity physical activity, which is often determined using step rate thresholds. Fixed step rate thresholds that correspond to moderate (MPA) and vigorous-intensity physical activity (VPA) have been developed for heuristic activity promotion. The activPAL monitor uses step rate thresholds to determine activity intensity. Stepping thresholds may also vary based on body mass index (BMI) or aerobic fitness level in older adults. Despite the various thresholds used in the literature, it is unclear whether they produce similar outcomes.Research QuestionHow does time spent in physical activity intensities compare between different step rate thresholds in older adults?MethodsThirty-eight participants (24♀; 67 ± 4 years; BMI: 26.6 ± 4.4 kg/m²) wore an activPAL monitor 24-hr/day for up to 7-d (total: 205-d). Aerobic fitness (V̇O₂max: 23 ± 8 ml/kg/min) was determined via indirect calorimetry during a maximal, graded cycling test. Time spent in each intensity category (light-physical-activity [LPA], MPA, VPA) was determined using the fixed (MPA/VPA) 100/130, 110/130, and activPAL step rate thresholds (74/212), as well as BMI-adjusted absolute (108.5 ± 2.5/134.0 ± 4.8) and BMI-adjusted relative (40%/60% V̇O₂max; 111.4 ± 14.7/132.0 ± 19.0) cut-offs. Times spent in each intensity category were compared between methods.ResultsThe activPAL and 100/130 thresholds yielded less LPA and more MPA than all other methods. The activPAL had no time spent in VPA at all. The BMI-adjusted absolute and relative thresholds produced statistically equivalent time in LPA and MPA (via equivalence testing), but not VPA. No two methods yielded similar time spent in LPA, MPA, or VPA.SignificanceThe choice of step rate threshold has a major impact on physical activity intensity outcomes in older adults. Inherently, strategies that adjust for older adults’ body size and/or aerobic fitness level provide a more individualized data processing strategy than fixed thresholds that assume the same threshold for all older adults     

人工智能在中医药领域的应用进展及现状思考

人工智能（Artificial Intelligence，AI）的蓬勃兴起为现代社会带来了前所未有的机遇，中医药是中华民族传承千年的文化瑰宝。随着人工智能技术不断在中医药领域的科技创新中崭露头角，二者的融合不断加深，人工智能在中医药领域的发展前景、争议挑战也引发了诸多思考。本文将从人工智能在中医药领域的应用入手，对人工智能辅助中医诊断、智能决策与数据挖掘、健康管理及中草药现代化研究等方面，就近年来国内外研究进展进行总结与分析，以期为AI视域下实现中医药现代化、智能化赋能。     

基于真实世界数据探讨中医药治疗糖尿病肾病用药规律的研究＊

目的 基于临床信息系统分析总结真实世界的数据，采用数据挖掘的方法探讨中医药治疗糖尿病肾病的用药规律。方法 收集2018年1月-2020年12月上海中医药大学附属曙光医院宝山分院健康信息系统确诊的糖尿病肾病的门诊或住院患者的诊疗信息，建立Excel数据库，采用Excel 2010软件统计高频药物的四气、五味、归经及功效；使用SPSS Modeler 18.0软件中的Apriori算法分析关联规则，采用web节点建构药对关联网状图；运用SPSS 25统计软件进行因子分析。结果 本研究最终纳入477例接受中药饮片治疗的DKD患者，在1203条方剂信息中，涉及中药462种；使用频数排名前5位的中药分别是黄芪、黄精、石斛、山茱萸、麦冬；使用频数前5类的中药类别分别是补气药、补阴药、清热燥湿药、活血调经药、息风止痉药；在30味高频药物中，苦、甘、辛药味最为常见；药性寒、温数量接近；归脾经、肝经、肺经、肾经居多；关联规则提示，药物组合中置信度最高的组合为地龙-当归-僵蚕，因子分析共得到5个有效因子，累积贡献率为47.33%。结论 中医药治疗DKD在补益气血阴阳的同时，兼顾对瘀血、湿邪和痰饮的治疗，结合证型，可考虑使用黄芪、黄精、石斛、山茱萸、麦冬、金蝉花等药物的使用，清热燥湿药物如黄连、黄芩、黄柏可适当加入，为使邪有去处，大黄、车前子或可增添疗效。     

Out of the boxes,out of the silos: The need of interdisciplinary collaboration to reduce poor-quality medical products in the supply chain

In this paper, we argue that understanding and addressing the problem of poor-quality medical products requires a more interdisciplinary approach than has been evident to date. While prospective studies based on rigorous standardized methodologies are the gold standard for measuring the prevalence of poor-quality medical products and understanding their distribution nationally and internationally, they should be complemented by social science research to unpack the complex set of social, economic, and governance factors that underlie these patterns. In the following sections, we discuss specific examples of prospective quality surveys and of social science studies, highlighting the value of cross-sector partnerships in driving high-quality, policy-relevant research in this area.     

Reference set for performance testing of pediatric vaccine safety signal detection methods and systems

BackgroundSafety signal detection in spontaneous reporting system databases and electronic healthcare records is key to detection of previously unknown adverse events following immunization. Various statistical methods for signal detection in these different datasources have been developed, however none are geared to the pediatric population and none specifically to vaccines. A reference set comprising pediatric vaccine-adverse event pairs is required for reliable performance testing of statistical methods within and across data sources.MethodsThe study was conducted within the context of the Global Research in Paediatrics (GRiP) project, as part of the seventh framework programme (FP7) of the European Commission. Criteria for the selection of vaccines considered in the reference set were routine and global use in the pediatric population. Adverse events were primarily selected based on importance. Outcome based systematic literature searches were performed for all identified vaccine-adverse event pairs and complemented by expert committee reports, evidence based decision support systems (e.g. Micromedex), and summaries of product characteristics. Classification into positive (PC) and negative control (NC) pairs was performed by two independent reviewers according to a pre-defined algorithm and discussed for consensus in case of disagreement.ResultsWe selected 13 vaccines and 14 adverse events to be included in the reference set. From a total of 182 vaccine-adverse event pairs, we classified 18 as PC, 113 as NC and 51 as unclassifiable. Most classifications (91) were based on literature review, 45 were based on expert committee reports, and for 46 vaccine-adverse event pairs, an underlying pathomechanism was not plausible classifying the association as NC.ConclusionA reference set of vaccine-adverse event pairs was developed. We propose its use for comparing signal detection methods and systems in the pediatric population.     

Bibliometric analysis of orthopaedic related publications by Indian authors from the last decade

PurposeBibliometric studies have been established methods of analysing publications on a particular topic. These studies have been done on various orthopaedic topics and are increasing. The advantages of these studies have been highlighted in previous publications. Although some studies have been done on Indian publications from other specialties, those analysing Indian Orthopaedic Publications are lacking.MethodsWe performed a search in Scopus to look for all publications related to orthopaedics from India. Our search strategy in Scopus included ((TITLE-ABS-KEY(Orthopaedics OR Orthopaedics) AND AFFIL(India)) AND PUBYEAR > 2009 AND PUBYEAR < 2020) which resulted in 3270 articles on 02/11/2021. We analyzed the most publishing universities, city, state, specialty, authors, and anatomic location of these publications. We also mined the data to draw word clouds based on data obtained from the titles of articles, keywords and the affiliations of each of the articles published.ResultsTamil Nadu and New Delhi and their institutes appear to be the epicenter of publication activities in Orthopaedics in India. There has been a healthy trend of growth of articles in the orthopaedic specialty. Since there is a significant overlap of technology and engineering, it is not surprising to see engineering and technology institutes among the top 10 published institutes and even journals for the publications on orthopaedics.ConclusionThere has been a steady increase in the number of publications in the last decade. New Delhi and its Universities and Institutes appear to contribute the majority of citations and publications related to orthopaedics. Journal of Clinical Orthopaedics and Trauma was the most publishing journal for Indian authors on Orthopaedic related articles.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Evaluating the inhibitory potential of Withania somnifera on platelet aggregation and inflammation enzymes: An in vitro and in silico study

Context Withania somnifera (L.) Dunal is traditionally used for treating various ailments, but lacks scientific evaluation.

Objective This study evaluates Withania somnifera (WS) for its effect on platelet activity and inflammatory enzymes.

Materials and methods Aqueous and ethanolic (1:1) leaf extracts were subjected to in vitro indirect haemolytic activity using Naja naja venom, human platelet aggregation was quantified for lipid peroxidation using arachidonic acid (AA) as agonist and 5-lipoxygenase (5-LOX) levels were determined using standard spectrometric assays. Further, molecular docking was performed by the ligand fit method using molegro software package (Molegro ApS, Aarhus, Denmark).

Results The study found that aqueous and ethanol extracts have very negligible effect (15%) with an IC₅₀ value of 13.8?mg/mL on PLA₂ from Naja naja venom. Further, extracts of WS also had very little effect (18%) with an IC₅₀ value of 16.6?mg/mL on malondialdehyde (MDA) formation. However, a 65% inhibition of 5-LOX with an IC₅₀ value of 0.92?mg/mL was observed in 1:1 ethanol extracts. The same was evident from SAR model with the active ingredient withaferin A binding predominantly on Phe 77, Tyr 98, Arg 99, Asp 164, Leu 168, Ser 382, Arg 395, Tyr 396 and Tyr 614 with an atomic contact energy value of??128.96 compared to standard phenidone (?103.61). Thus, the current study validates the application of WS for inflammatory diseases.

Conclusion This study reveals the inhibitory potential of W. somnifera on inflammatory enzymes and platelet aggregation. Thus, WS can serve as a newer, safer and affordable medicine for inflammatory diseases.     

三级公立医院行政总值班管理现状分析与研究*

探索一套符合现代化医院的行政总值班管理体系，确保医院管理工作安全、稳定运行。分析国内外医院总值班现状和问题，运用系统性、科学性、规范性等综合干预措施，进一步提高值班人员责任意识、分析问题和解决问题能力等综合管理能力，使总值班管理从粗放式向精细化转变，实现目标标准化、过程规范化、结果同质化的服务格局。