The development of molecular medicine has greatly promoted the research and development (R&D) of innovative drugs. However,drug design and development for those novel targets remains a big challenge with low succe...The development of molecular medicine has greatly promoted the research and development (R&D) of innovative drugs. However,drug design and development for those novel targets remains a big challenge with low success rates and high attrition of drug candidates1. The current methodology of new drug R&D is deeply influenced by the idea of allopathic medicine, which directly inhibits biological targets.展开更多
目的:基于健康行动过程取向(health action process approach,HAPA)模型探究2型糖尿病患者膳食模式和血糖控制的影响因素。方法:选取山东省东营市东城医院下属的11个社区卫生中心内的2型糖尿病患者作为研究对象,通过静脉抽血检测患者的...目的:基于健康行动过程取向(health action process approach,HAPA)模型探究2型糖尿病患者膳食模式和血糖控制的影响因素。方法:选取山东省东营市东城医院下属的11个社区卫生中心内的2型糖尿病患者作为研究对象,通过静脉抽血检测患者的糖化血红蛋白(glycosylated hemoglobin,HbA1c)水平,采用一般资料调查表、膳食频率调查问卷、2型糖尿病自我管理量表、HAPA量表收集信息。通过因子分析将患者的膳食模式划分为不同类型,通过结构方程模型分析HAPA模型各维度对于患者膳食模式和血糖控制的影响。结果:共纳入819例2型糖尿病患者,总体HbA1c水平为7.1%±1.1%。研究对象的总体饮食管理得分为5.0(1.0,7.0),具体日常饮食被划分为中低血糖生成指数(glycemic index,GI)膳食模式、肉类膳食模式、水果膳食模式、高GI/淀粉类膳食模式、蛋奶类膳食模式。结构方程模型结果显示,积极结果预期(β=0.417,P<0.001)、消极结果预期(β=-0.239,P<0.001)和感知风险严重性(β=0.075,P=0.036)影响饮食管理行为意向;饮食管理行为意向影响行动计划(β=0.531,P<0.001)和应对计划(β=0.228,P<0.001);行动计划影响总体饮食管理行为(β=0.183,P<0.001);总体饮食管理行为影响中低GI膳食模式(β=0.133,P<0.001)、水果膳食模式(β=-0.103,P=0.003)、高GI/淀粉类膳食模式(β=-0.110,P=0.002)和蛋奶类膳食模式(β=0.076,P=0.031);中低GI膳食模式(β=-0.086,P=0.013)、肉类膳食模式(β=0.084,P=0.015)影响HbA1c水平。此外,行动自我效能可以影响行为意向(β=0.384,P<0.001)、行动计划(β=0.122,P=0.006)和应对计划(β=0.146,P=0.001)。维持自我效能可以影响行动计划(β=0.170,P<0.001)、应对计划(β=0.408,P<0.001)和总体饮食管理行为(β=0.265,P<0.001)。结论:纳入的2型糖尿病患者的膳食模式存在差异,且每周饮食管理的水平欠佳,HAPA模型对于2型糖尿病患者的膳食模式和血糖控制水平具有较好的解释作用,因此,今后可基于HAPA模型制定针对性的饮食干预,提高患者的总体饮食管理水平,促进患者养成低GI的健康膳食模式,从而改善血糖水平,提高生活质量。展开更多
How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable i...How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].展开更多
目的运用数据挖掘与网络药理学方法,系统分析中药治疗心功能不全的组方用药规律及其潜在机制,为中医药干预心功能不全提供理论依据与研究思路。方法检索中国知网、万方数据知识服务平台、维普网、中国生物医学文献数据库、Web of Scienc...目的运用数据挖掘与网络药理学方法,系统分析中药治疗心功能不全的组方用药规律及其潜在机制,为中医药干预心功能不全提供理论依据与研究思路。方法检索中国知网、万方数据知识服务平台、维普网、中国生物医学文献数据库、Web of Science核心合集数据库、Pub Med、Embase及Cochrane Library中英文数据库,收集建库至2025年1月公开发表的中医药治疗心功能不全的相关方剂文献,筛选后构建方剂数据库。利用古今医案云平台(V2.3.7)进行数据挖掘,提取药物四气、五味、归经,药物使用频次及配伍规律等核心观察指标;结合传统中医药系统药理学数据库与分析平台、本草组鉴数据库及相关文献筛选核心方药及其活性成分,并通过SwissTargetPrediction等工具预测药物靶点。联合Gene Cards、OMIM、Drug Bank、TTD数据库获取心功能不全疾病相关靶点,筛选共同靶点并构建“中药-成分-靶点-疾病”网络,采用Cytoscape3.10.2构建蛋白质-蛋白质相互作用(PPI)网络,通过Metascape平台开展基因本体(GO)和京都基因和基因组数据库(KEGG)富集分析,并采用分子对接方法验证核心成分与关键靶点的结合活性。结果共纳入228首方剂,涵盖218味中药,用药以温、平、微寒为主,药味多为甘、辛、苦,归经主要集中于肺、心经和脾经。常见功效包括生津养血、利水消肿、活血祛瘀等。其中“黄芪→丹参”的共现频率最高,黄芪、丹参、附子、茯苓、麦冬、炙甘草为高频药物,构成核心处方。网络药理学分析共筛选得104个活性成分,969个作用靶点,与疾病相关重叠靶点354个。PPI网络分析筛选出10个核心靶点基因,分别为Akt1、TNF、BCL2、CASP3、ESR1、STAT3、MMP9、PTGS2、IL-6和JUN。GO富集分析显示,这些靶点主要参与细胞运动调控、激素应答与循环系统过程等生物过程。KEGG通路富集分析揭示其显著富集于TNF、MAPK、m TOR、VEGF等信号通路。分子对接结果提示,关键化合物对核心靶点有良好结合活性。结论中医药治疗心功能不全的组方用药整体体现出“益气活血、温阳利水”的基本规律。核心成分可能通过多靶点、多通路协同作用干预细胞凋亡、心肌重构等关键病理过程,从而发挥综合治疗效应,为中医药防治心功能不全提供潜在机制依据。展开更多
文摘The development of molecular medicine has greatly promoted the research and development (R&D) of innovative drugs. However,drug design and development for those novel targets remains a big challenge with low success rates and high attrition of drug candidates1. The current methodology of new drug R&D is deeply influenced by the idea of allopathic medicine, which directly inhibits biological targets.
文摘How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].
