首页|Reports on Machine Learning Findings from University Hospital Provide New Insights (Predictive Model for Vitamin C Levels In Hyperinsulinemic Individuals Based On Age, Sex, Waist Circumference,Low-density Lipoprotein, and Immune-associated ...)
Reports on Machine Learning Findings from University Hospital Provide New Insights (Predictive Model for Vitamin C Levels In Hyperinsulinemic Individuals Based On Age, Sex, Waist Circumference,Low-density Lipoprotein, and Immune-associated ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting originating from Quebec City, Canada, by NewsRx correspondents, research stated, "Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects." Funders for this research include Canadian Institutes of Health Research (CIHR), New Initiative Funds from the Endocrinology/Nephrology axis at the Centre de Recherche du CHU de Quebec, Fondation du CHU de Quebec, Fonds de recherche du Quebec-Sante (FRQ-S). Our news editors obtained a quote from the research from University Hospital, "However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects."
Quebec CityCanadaNorth and Central AmericaAscorbic AcidCyborgsEmerging TechnologiesHydroxy AcidsLipid ResearchLipidsLipoproteinsMachine LearningPeptides and ProteinsRisk and PreventionSugar AcidsUniversity Hospital
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