Advances in the application of multi-omics and machine learning technologies in sepsis research - Chen Pengpeng; Yang Jie; Jin Xinhao; Zhang Bo; Yang Suibi; Hong Yucai; Ni Hongying; Zhang Zhongheng (生物医学转化)
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.