Objectives: The burgeoning prevalence of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) within Africa is concerning. Machine learning (ML) techniques offer a unique opportunity to leverage data-driven insights and construct predictive models for MetS risk, thereby enhancing the implementation of personalised prevention strategies. In this work, we employed ML techniques to develop predictive models for pre-MetS and MetS among diabetic patients.
View Article and Find Full Text PDFObjective: Rhizome (CLR), due to its potent antioxidant phytochemical constituents, was investigated for its effects on bisphenol A (BPA)-induced cardiovascular and renal damage.
Materials And Methods: Sixty rats were randomly selected, and grouped as control, BPA (100 mg/ kg), BPA and CLR 100 mg/kg, BPA and CLR 200 mg/kg, CLR 100 mg/kg, and CLR 200 mg/kg for 21 days. Oxidative stress indices, antioxidant status, blood pressure parameters, genotoxicity, and immunohistochemistry were determined.