多模态组学与机器学习技术在脓毒症研究中的应用进展  

Advances in the application of multi-omics and machine learning technologies in sepsis research

作　　者：陈棚棚 杨杰[1] 金信浩 张菠 杨穗碧 洪玉才[1] 倪红英[3] 章仲恒[1,4,5] Chen Pengpeng;Yang Jie;Jin Xinhao;Zhang Bo;Yang Suibi;Hong Yucai;Ni Hongying;Zhang Zhongheng(Department of Emergency Medicine,Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310000,Zhejiang,China;Department of Intensive Care Unit,Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310000,Zhejiang,China;Department of Critical Care Medicine,Jinhua Municipal Central Hospital,Jinhua 321036,Zhejiang,China;Key Laboratory of Precision Diagnosis and Treatment for Abdominal Infections,Hangzhou 310000,Zhejiang,China;School of Medicine,Shaoxing University,Shaoxing 312000,Zhejiang,China)

机构地区：[1]浙江大学医学院附属邵逸夫医院急诊医学科,浙江杭州310000 [2]浙江大学医学院附属邵逸夫医院重症医学科,浙江杭州310000 [3]金华市中心医院重症医学科,浙江金华321036 [4]浙江省腹腔感染精准诊疗重点实验室,浙江杭州310000 [5]绍兴文理学院医学院,浙江绍兴312000

出　　处：《生物医学转化》2024年第4期45-57,共13页Biomedical Transformation

基　　金：国家重点研发计划项目(2023YFC3603104,2022YFC2504503);国家自然科学基金项目(82272180,82472243);浙江大学龙泉创新中心项目(ZJDXLQCXZCJBGS2024016);浙江省自然科学基金华东医药企业创新发展联合基金资助项目(LHDMD24H150001);国家中医药管理局科技司-浙江省中医药管理局共建科技计划(GZY-ZJ-KJ-24082);浙江省医药卫生科技计划项目(2024KY1099);金华市科技局重点科技计划(2024-3-047)。

摘　　要：脓毒症是全球范围内的重大健康挑战之一,其复杂的病理机制和多器官功能损伤对患者生命构成了严重威胁。近年来,多模态组学技术结合机器学习在脓毒症研究中取得了显著突破,为早期诊断、精准治疗和个性化干预提供了新的前景。传统治疗手段如皮质类固醇、液体管理及抗生素的个性化调整,以及中医药与乌司他丁等在多模态组学研究中的应用,拓宽了脓毒症治疗的选择空间。中国脓毒症多模态组学研究联盟(CMAISE)通过整合基因组学、蛋白质组学、代谢组学等多模态组学数据,深入探索脓毒症的分子机制及生物标志物。本综述全面总结了多模态组学技术在脓毒症研究中的应用现状,并探讨了机器学习在个性化治疗中的潜力,为未来的临床应用及科研发展提供了理论依据和思路。Sepsis is one of the major global health challenges,with its complex pathological mechanisms and multi-organ dysfunction posing serious threats to patient survival.In recent years,the combination of multiomics technologies and machine learning has led to significant breakthroughs in sepsis research,providing new prospects for early diagnosis,precise treatment,and personalized interventions.The personalized adjustments of traditional treatments,such as corticosteroids,fluid management,and antibiotics,along with the application of traditional Chinese medicine and ulinastatin in multi-omics studies,have expanded the therapeutic options for sepsis.Chinese Multi-omics Advances in Sepsis(CMAISE) integrates multi-omics data,including genomics,proteomics,and metabolomics,to explore the molecular mechanisms and biomarkers of sepsis.This review comprehensively summarizes the current applications of multi-omics technologies in sepsis research and explores the potential of machine learning in personalized treatment,offering theoretical foundations and insights for future clinical applications and research development.

关 键 词：脓毒症 多模态 组学 

分 类 号：R631[医药卫生—外科学]