首页|General Hospital of Northern Theater Command Reports Findings in Artificial Inte lligence (Integrated multi-omics and artificial intelligence to explore new neut rophils clusters and potential biomarkers in sepsis with experimental validation )
General Hospital of Northern Theater Command Reports Findings in Artificial Inte lligence (Integrated multi-omics and artificial intelligence to explore new neut rophils clusters and potential biomarkers in sepsis with experimental validation )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Liaoning, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Sepsis, causi ng serious organ and tissue damage and even death, has not been fully elucidated . Therefore, understanding the key mechanisms underlying sepsis-associated immun e responses would lead to more potential therapeutic strategies." Our news journalists obtained a quote from the research from the General Hospita l of Northern Theater Command, "Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal traj ectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods wer e used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative rever se transcription-polymerase chain reaction was to validate mRNA expression of hu b genes in peripheral blood neutrophils of two mice sepsis models. We discovered two novel neutrophil subtypes with a significant increase under sepsis. These t wo neutrophil subtypes were enriched in the late state during neutrophils differ entiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy an d robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). T he SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were sig nificant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutro phils of CLP and LPS-induced sepsis mice models. Our research discovered new clu sters of neutrophils in sepsis."
LiaoningPeople's Republic of ChinaAs iaArtificial IntelligenceBiomarkersBlood CellsBlood Diseases and Conditi onsBloodstream InfectionCyborgsDiagnostics and ScreeningEmerging Technol ogiesGeneticsGranulocytesHealth and MedicineHemic and Immune SystemsIm munologyMachine LearningNeutrophilsPhagocytesSepsisSepticemia
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