Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”co...Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.展开更多
目的通过评价中药复方防治胃癌前病变(PLGC)动物实验研究的方法学质量及报告质量,分析实验过程中的偏倚风险及研究报告的不足,为提高中药复方防治PLGC动物实验研究质量提供参考。方法计算机检索中国知识资源总库(CNKI)、万方数据知识服...目的通过评价中药复方防治胃癌前病变(PLGC)动物实验研究的方法学质量及报告质量,分析实验过程中的偏倚风险及研究报告的不足,为提高中药复方防治PLGC动物实验研究质量提供参考。方法计算机检索中国知识资源总库(CNKI)、万方数据知识服务平台(WanfangData)、中文科技期刊数据库(VIP)、中国生物医学文献数据库(CBM)、PubMed、Cochrane Library、Web of Science和Embase数据库2014年1月1日-2024年2月23日发表的关于中药复方防治PLGC动物实验文献,采用SYRCLE工具和ARRIVE2.0指南对纳入文献进行评分并计算各条目“低风险”符合率。结果共纳入文献213篇,其中中文文献189篇、英文文献24篇。SYRCLE工具评分为(12.86±1.29)分,“低风险”符合率为32.79%。ARRIVE2.0指南必备条目评分为(24.15±2.80)分,“低风险”符合率为49.08%;推荐条目评分为(11.28±3.40)分,“低风险”符合率为30.27%。SYRCLE工具评价中,144项(67.61%)研究未详细阐述分配序列产生的方法,所有研究均未描述分配隐藏充分与否及实施偏倚过程中的盲法,7项(3.29%)研究描述对结果评价者施盲。ARRIVE2.0指南中,所有研究均未报告样本量的确定方法、均未提供用于确定样本量的结局指标及实验方案注册声明,51项(23.94%)研究明确提出PLGC造模成功标准,66项(30.96%)研究提供所使用统计方法的详细信息,29项(13.62%)研究提供完整的伦理声明,22项(10.33%)报告了利益冲突。结论2014-2024年发表的中药复方防治PLGC动物实验文献方法学质量及报告质量存在较多问题,尤其是在实验过程中随机盲法策略的实施、样本量计算细节及纳入排除标准报告等方面存在缺陷,建议今后研究参考SYRCLE工具及ARRIVE2.0指南清单,以优化研究方案和报告,提高PLGC动物实验研究结果的可信度与规范性。展开更多
基金supported by the National Key Research and Development Program of China(No.2024YFC3506900)Science and Technology Program of Tianjin(No.24ZXZSSS00460)Special Project for Technological Innovation in New Productive Forces of Modern Chinese Medicines(No.24ZXZKSY00010)。
文摘Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.
文摘目的通过评价中药复方防治胃癌前病变(PLGC)动物实验研究的方法学质量及报告质量,分析实验过程中的偏倚风险及研究报告的不足,为提高中药复方防治PLGC动物实验研究质量提供参考。方法计算机检索中国知识资源总库(CNKI)、万方数据知识服务平台(WanfangData)、中文科技期刊数据库(VIP)、中国生物医学文献数据库(CBM)、PubMed、Cochrane Library、Web of Science和Embase数据库2014年1月1日-2024年2月23日发表的关于中药复方防治PLGC动物实验文献,采用SYRCLE工具和ARRIVE2.0指南对纳入文献进行评分并计算各条目“低风险”符合率。结果共纳入文献213篇,其中中文文献189篇、英文文献24篇。SYRCLE工具评分为(12.86±1.29)分,“低风险”符合率为32.79%。ARRIVE2.0指南必备条目评分为(24.15±2.80)分,“低风险”符合率为49.08%;推荐条目评分为(11.28±3.40)分,“低风险”符合率为30.27%。SYRCLE工具评价中,144项(67.61%)研究未详细阐述分配序列产生的方法,所有研究均未描述分配隐藏充分与否及实施偏倚过程中的盲法,7项(3.29%)研究描述对结果评价者施盲。ARRIVE2.0指南中,所有研究均未报告样本量的确定方法、均未提供用于确定样本量的结局指标及实验方案注册声明,51项(23.94%)研究明确提出PLGC造模成功标准,66项(30.96%)研究提供所使用统计方法的详细信息,29项(13.62%)研究提供完整的伦理声明,22项(10.33%)报告了利益冲突。结论2014-2024年发表的中药复方防治PLGC动物实验文献方法学质量及报告质量存在较多问题,尤其是在实验过程中随机盲法策略的实施、样本量计算细节及纳入排除标准报告等方面存在缺陷,建议今后研究参考SYRCLE工具及ARRIVE2.0指南清单,以优化研究方案和报告,提高PLGC动物实验研究结果的可信度与规范性。
