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ABSTRACT: Background
Multiple sclerosis (MS) is a chronic debilitating disorder characterized by persisting damage to the brain caused by autoreactive leukocytes. Leukocyte activation is regulated by cytokines, which are readily detected in MS serum and cerebrospinal fluid (CSF).Objective
Serum and CSF levels of forty-five cytokines were analyzed to identify MS diagnostic markers.Methods
Cytokines were analyzed using multiplex immunoassay. ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS.Results
Twenty-two and twenty cytokines were altered in CSF and serum, respectively. The MS diagnosis accuracy was ≥92% when any randomly selected five
SUBMITTER: Martynova E
PROVIDER: S-EPMC7607285 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature