目的总结全麻术后患者口渴护理管理的最佳证据。方法计算机检索美国国家指南库、JBI循证卫生保健中心数据库、英格兰校际指南网站、英国国家卫生与临床优化研究所、加拿大安大略护理学会指南网、维普、万方、中国知网、UpToDate、PubMed...目的总结全麻术后患者口渴护理管理的最佳证据。方法计算机检索美国国家指南库、JBI循证卫生保健中心数据库、英格兰校际指南网站、英国国家卫生与临床优化研究所、加拿大安大略护理学会指南网、维普、万方、中国知网、UpToDate、PubMed、Web of Science、Embase、Cochrane Library等数据库中关于全麻术后口渴相关的文献,由2名研究人员独立完成质量评价,并进行证据提取及汇总。结果共纳入6篇文献,包括3篇指南、2篇最佳实践、1篇系统评价。汇总了风险因素、口渴评估、早期预防、症状管理、教育培训5个方面共20条证据。结论本研究纳入的文献总体质量较高,但纳入文献大部分来源于国外,护理人员应结合科室现状及患者意愿将全麻术后口渴护理管理方案转化为临床实践,以减轻患者口渴感。展开更多
Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug interactions.Most existing models rely on drug sim...Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug interactions.Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action.In this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction networks.We explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning approaches.In unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group interactions.In addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug interactions.Our method exceeds existing approaches in terms of performance.Overall,our experiments demonstrate the effectiveness and practicability of the proposed similarity measure.On the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown MoA.On the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.展开更多
文摘目的总结全麻术后患者口渴护理管理的最佳证据。方法计算机检索美国国家指南库、JBI循证卫生保健中心数据库、英格兰校际指南网站、英国国家卫生与临床优化研究所、加拿大安大略护理学会指南网、维普、万方、中国知网、UpToDate、PubMed、Web of Science、Embase、Cochrane Library等数据库中关于全麻术后口渴相关的文献,由2名研究人员独立完成质量评价,并进行证据提取及汇总。结果共纳入6篇文献,包括3篇指南、2篇最佳实践、1篇系统评价。汇总了风险因素、口渴评估、早期预防、症状管理、教育培训5个方面共20条证据。结论本研究纳入的文献总体质量较高,但纳入文献大部分来源于国外,护理人员应结合科室现状及患者意愿将全麻术后口渴护理管理方案转化为临床实践,以减轻患者口渴感。
基金National Natural Science Foundation of China,Grant/Award Number:62372208,61772226Science and Technology Development Program of Jilin Province,Grant/Award Number:20210204133YY。
文摘Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug interactions.Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action.In this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction networks.We explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning approaches.In unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group interactions.In addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug interactions.Our method exceeds existing approaches in terms of performance.Overall,our experiments demonstrate the effectiveness and practicability of the proposed similarity measure.On the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown MoA.On the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.
