Make in India: Normative data for automated perimetry

Background:The normative data set in authomated perimetry is predominantly of non-Indian origin and hence may not be an accurate basis for visual field analysis in Indian population. This video describes an attempt to create a native normative dataset for automated perimetry, which can then be fed in our machines and be used as the normative database.Purpose:To formulate normative data and to increase domain knowledge of normative values for automated perimetry in Indian population of different age groups.Synopsis:Cross-sectional study conducted on patients receiving outpatient care in a span of 3 years, which included 6586 healthy normal patients (13172 eyes) with vision 6/6 unaided or after refractive correction. The patients were tested with 30-2 SITA FAST threshold algorithm on Humphrey Field Analyzer Model no: 745i. Normative data was calculated on basis of age group ranging from 19-75 years categorized to every decade. Normal values were formulated on basis of perimetry performed on normal patients.Highlights:Our work on creating a native normative dataset may add value as well as increase the accuracy of perimetry analysis in Indian eyes.Online Video Link: https://youtu.be/jqgC2Tn7HIg     

Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study

BackgroundSocial media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research.ObjectiveWe aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet.MethodsTweets containing a specific lexicon relating to suicide were filtered through Twitter''s public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed.ResultsOf 2509 tweets, 8.61% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness (ρ=0.266; P<.001), joy (ρ=–0.234; P=.001), general risk (ρ=0.908; P<.001), and intensity of suicidal ideation (ρ=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time (β=0.311; P=.001), as well as being a predictor for fear (β=–0.009; P=.01) and emotional valence (β=0.007; P=.009). The model explained 75% of the variance.ConclusionsThese findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media.     

基于数据挖掘方法的椎动脉型颈椎病用药规律研究

目的:运用中医传承辅助平台分析椎动脉型颈椎病中药处方的用药规律。方法:通过中国知网、重庆维普、万方数据库中收集2010年1月—2014年12月治疗椎动脉型颈椎病的中药处方,采用关联规则算法、复杂系统熵聚类等无监督数据挖掘方法,分析处方中各种药物的使用频次、性味归经、药物之间的关联规则及核心药物组合。结果:对筛选出的118个中药处方进行分析,确定其常用药物频次,发现常用药物关联规则共57个,核心组合30个。结论:椎动脉型颈椎病常用的药物以活血化瘀药、温经通络药和祛风止眩药为主,四气分布以温、平、凉为主,五味分布以甘、辛、苦为主,主要归于肝、脾、心经,使用频次较高的药味为川芎、葛根、当归、天麻、丹参等。     

文本挖掘探索参麦注射液的临床应用特点研究

目的探索参麦注射液临床应用规律。方法应用文献检索方法检索（1980年1月1012年5月）中国生物医学文献数据库收集参麦注射液文献数据,采用基于关键词频数统计的数据分层算法,挖掘数据集中证型、疾病、症状、中成药、西药等方面。挖掘结果通过一维频次及构建网络图进行展示。结果共有3159篇文献,结果显示参麦注射液多与虚、瘀证型因素相关;心力衰竭、心律失常、心肌炎、心肌梗死、休克是其治疗的核心疾病;主要治疗心绞痛、乏力、胸闷胸痛等症状;常与丹参注射液、复方丹参注射液、黄芪注射液等中成药合用;常与硝酸甘油、果糖、卡托普利等西药合用。结论参麦注射液治疗的证型、疾病挖掘结果与其说明书中一致,潜在治疗证型为瘀证,潜在治疗疾病为心力衰竭、心律失常、心肌梗死等。其常与丹参注射液、复方丹参注射液、黄芪注射液等中成药合用和硝酸甘油、果糖、卡托普利等西药合用。     

基于纵向和生存时间结局的联合模型方法及应用

目的：随着医学研究中指标维度、数量和数据类型的日渐增多,科学地联合纵向评价指标和事件发生时间资料,准确评估结局事件发生率偏低时的试验效果,对客观正确地评价整体治疗方案的效果有重要意义。方法：首先讨论基于纵向评价指标和生存时间资料的传统联合模型的适用范围、优点与局限性,然后放宽传统联合模型假设,改进模型应用于事件发生率过低的情况,最后对缺血性中风早期治疗数据进行分析。结果：中医组治疗早期缺血性中风的效果明显优于西医组;NIHSS量表得分与中风治愈率和治疗时间有显著关系：患者入组时NIHSS得分越高,治愈率越低;治疗过程中NIHSS分数降低越明显,治愈率越高,所需治疗时间越短。结论：联合模型可用于多类型结局指标的联合评价,用于临床试验中疗效和安全性的综合分析,同时缺血性中风早期治疗中NIHSS得分可辅助预测治疗效果。     

刘凤斌教授治疗慢性萎缩性胃炎的病例系列挖掘分析和经验总结

为总结刘凤斌教授中医药治疗慢性萎缩性胃炎的理论思想和临床经验,采用回顾性病例系列研究和专家访谈结合设计,首先回顾2009—2013年在广州中医药大学第一附属医院就诊的慢性萎缩性胃炎患者资料,提取所用中药、疾病、证型、医疗费用和药物数量等信息,数据整理后采用统计描述和二分类Logistic回归分析确定证型和初步的主药及加减变化,统计时同时进行全集和亚组分析;然后进行专家访谈讨论形成最终的证型及用药。结果共收集到228例慢性萎缩性胃炎患者资料,年龄(50.30±10.18)岁,男性151例(66.23%),女性77例(33.77%)。157例患者提取到中医诊断和证型信息,其中胃痞和胃脘痛各115和23例,缺失19例,包含脾胃虚弱证、脾虚湿热证、脾胃不和证、肝郁脾虚证、肝胃不和证、气阴不足证患者12,57,18,23,21,26例。整体228例患者共用104味中药,亚组157例患者共用94味中药,总结各组前15味中药形成初步的治疗主药和加减变化。然后,使用亚组157例患者资料,以各证型为因变量,纳入前32味中药进行Logistic回归分析,形成各证型下的加减用药变化。专家评阅修改后认为病因与饮食、情绪、体虚有关,病机是"脾虚气滞,热郁络瘀",并确定以上6种证型和12味治疗主药,包括太子参、茯苓、白术、甘草、浙贝母、海螵蛸、大腹皮、枳壳、紫苏梗、白花蛇舌草、半枝莲、醋莪术。该研究基于临床实践数据和专家访谈信息总结了刘凤斌教授中医药治疗慢性萎缩性胃炎的思想经验,设计严谨,具有良好的科学性和实用性。     

中药功效数据集市的多维分析和数据挖掘研究

为获得中药功效和相关研究文献中的显性和隐性的知识,提出建设综合中药功效、植物学分类、化学成分、现代药理作用和临床应用方面相关数据的中药功效数据集市;然后建立同一功效的数据立方体,利用多维数据分析和关联数据挖掘方法,剖析相关数据特征,发掘中药功效与植物学分类、化学成分、药理作用之间的潜在联系,探寻中药功效的实质.     

基于CiteSpace知识网络图谱的中药复方量效关系可视化分析

目的 探究中药复方量效关系这一领域的研究现状、研究热点和未来前沿,为今后的研究提供参考。方法 收集中国知网（CNKI）、万方（Wanfang）、维普（VIP）自建库至2022年9月16日与中药复方量效关系相关的文献,通过Note Express V3.6进行文献数据整合并建立中药复方量效关系数据库,以文献计量学为理论支持,利用Cite Space6.1.R3 Advanced最新版本绘制3D图谱,微软Excel 2016列表,对发文趋势、作者、机构、关键词、爆发词、术语等内容进行可视化分析。结果 最终纳入文献2656篇。年度发文量整体呈波动型上升,并在2010年达到顶峰。共有7606名学者参与了该领域的研究,其中仝小林院士、傅延龄教授发文量与平均被引次数较高,为中药复方量效关系研究领域的核心学者,具有较高影响力。涉及690个机构,北京中医药大学为领头机构。经关键词、术语等综合分析显示,“经方”“药对”“实验研究”“数据挖掘”为研究热点,而“药对”“数据挖掘”“半夏”近几年呈现较高爆发强度,预计成为该领域的前沿。另外,建议以中药配伍“十八反”中功效-剂量-毒性拐点的研究为切入点,拓展该领...     

数据挖掘在中医治疗抑郁症方药文献研究中的应用

目的：探讨数据挖掘在中医治疗抑郁症方药文献研究中的应用。方法：运用现代的数据统计方法如关联分析、聚类分析等,对历代郁证及近代文献治疗抑郁症方药进行数据挖掘。结果：运用现代的数据统计对历代郁证及近代文献治疗抑郁症方药进行数据挖掘,分析其性味归经、剂量、药物配伍及其与症状对应关系,通过分析整体数据,发掘其蕴藏的潜在规律,为优化组方配伍,指导临床运用提供依据。结论：抑郁症是一种常见的精神障碍疾病,历代医家在中医药治疗郁证方面积累了丰富的经验,数据挖掘拓展了中医药治疗抑郁症的研究思路,对推动中医药理论的继承和创新具有重要的意义。