首页|Guizhou University Reports Findings in Sepsis (Unraveling the genetic and molecu lar landscape of sepsis and acute kidney injury: A comprehensive GWAS and machin e learning approach)
Guizhou University Reports Findings in Sepsis (Unraveling the genetic and molecu lar landscape of sepsis and acute kidney injury: A comprehensive GWAS and machin e learning approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Blood Diseases and Con ditions-Sepsis is the subject of a report. According to news reporting origina ting from Guiyang, People's Republic of China, by NewsRx correspondents, researc h stated, "This study aimed to explore the underlying mechanisms of sepsis and a cute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent c omplication in critically ill sepsis patients. GWAS data was analyzed for geneti c association between AKI and sepsis." Our news editors obtained a quote from the research from Guizhou University, "Th en, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, ass essing their diagnostic and prognostic value through ROC curves and survival ana lysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes. LDSC confirmed a posit ive genetic correlation between AKI and sepsis, although no significant shared l oci were found. Bidirectional MR analysis indicated mutual increased risks of AK I and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differ ences in immune cells, immune-related pathways, HLA, and checkpoint genes were f ound between high- and low-risk groups. The study identified 62 potential drug t reatments for sepsis and AKI and constructed a ceRNA network. The identified sig nature genes hold potential clinical applications, including prognostic evaluati on and targeted therapeutic strategies for sepsis and AKI."
GuiyangPeople's Republic of ChinaAsi aBlood Diseases and ConditionsBloodstream InfectionCyborgsEmerging Techn ologiesGeneticsHealth and MedicineImmunologyMachine LearningSepsisSe pticemia
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