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ABSTRACT: Objective
This study aimed to leverage machine learning algorithms to explore the relationship between anti-double-stranded DNA (anti-dsDNA) immunoglobulin G (IgG) glycosylation and the degree of organ involvement in patients with SLE.Methods and analysis
We enrolled 86 consecutive treatment-naïve patients with SLE positive for anti-dsDNA antibodies from the Department of Rheumatology and Immunology at Ruijin Hospital, Shanghai, between 2017 and 2019. We quantified and classified the degree of organ involvement in patients with SLE and analysed each glycoform and a combination of glycoforms of purified anti-dsDNA IgG. A random forest classifier and artificial neural network were applied to evaluate the correlation between the levels of glycoform pairs and the degree of orga
SUBMITTER: Zhou Z
PROVIDER: S-EPMC12414148 | biostudies-literature | 2025 Sep
REPOSITORIES: biostudies-literature