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This study investigates the role of antioxidant and anti-inflammatory biomarkers in diagnosing multiple sclerosis (MS) using machine learning techniques. The findings indicate that lower levels of zinc, adiponectin, total radical-trapping antioxidant parameter (TRAP), and sulfhydryl groups, along with higher levels of advanced oxidation protein products (AOPP), are significant indicators of MS. A combination of these biomarkers demonstrates high sensitivity and specificity for MS diagnosis, suggesting poten

Author

Farhad Nourmohammadi

Language

EN