首页|Capital Medical University Reports Findings in Bioinformatics (Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays)
Capital Medical University Reports Findings in Bioinformatics (Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Septic patient s with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, th is study aimed to pinpoint potential genes related to sepsis with T2DM and devel op a predictive model for the disease.” Our news journalists obtained a quote from the research from Capital Medical Uni versity, “The candidate genes were screened using protein-protein interaction ne tworks (PPI) and machine learning algorithms. The nomogram and receiver operatin g characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blot ting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine le arning algorithms, demonstrating strong predictive capabilities. These biomarker s presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mo use models and blood samples were consistent with the findings of the bioinforma tics analysis. The study elucidated the common molecular mechanisms associated w ith the pathogenesis of T2DM and sepsis and developed a gene signature-based pre diction model for sepsis.”
BeijingPeople’s Republic of ChinaAsi aBioinformaticsBiomarkersBiotechnologyBlood Diseases and ConditionsBlo odstream InfectionCyborgsDiagnostics and ScreeningEmerging TechnologiesG eneticsHealth and MedicineInformation TechnologyMachine LearningSepsisSepticemia
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