This study aimed to identify a panel of biomarkers that would increase the precision of distinguishing patients in relapse compared to sNfL alone. Findings identified four blood protein including sNFL, uPA, hK8 and DSG3 that accurately distinguished relapse from remission in RRMS patients, providing an improved biomaker panel for monitioring disease activity beyond traditional measures.
Read MoreThis study shows IFN-B treatment in relapsing remitting multiple sclerosis increases transitional and regualatory B-cell, enhancing IL-10 production. Findings highlight IFN-B's role in regulating immune responses, with significant implications for MS therapy and disease management.
Read MoreThis study evaluated serum cytokine profiles for their utility to determine the heterogeneous responses to interferon (IFN)-β treatment in patients with multiple sclerosis (MS). Findings revealed that six cytokine-based subgroups with varied IFN-B treatment responses. Some subsets showed poor outcomes, while others had reduced relapses, highlighting immune heterogeneity linked to therapy effectiveness.
Read MoreThis decentralized study used machine learning to assess patient-reported outcomes, quality-of-life measures, and smartwatch biometric data from SLE patients. Models achieved significant predictive accuracy for disease flares, supporting proactive clinical screening.
Read MoreThis pilot study evaluates a comprehensive SLE management platform combining a Lupus Flare Risk Index biomarker, smartwatch-interfaced mobile app, and health coaching to improve patient self-efficacy and disease outcomes.
Read MoreThis study evaluated generative AI using ACR 1997 criteria to predict systemic lupus erythematosus classification from medical records. Results showed 76% predictive accuracy, with criterion-specific performance varying by clinical complexity.
Read MoreThis study assessed generative AI's ability to extract systemic lupus erythematosus classification criteria from medical records. Results showed structured criteria and digital profiling enhance diagnostic accuracy and clinical utility.
Read MoreThis study shows immune changes appear years before lupus is diagnosed: “type II interferon” and certain blood signals rise first, autoantibodies build next, and “type I interferon” spikes closest to diagnosis; combining these markers accurately flags high-risk individuals for earlier evaluation.
Read MoreThis study followed relatives of people with lupus and found those who later developed the disease already had higher “inflammation” signals in blood and lower “calming” signals; two markers, SCF (higher) and TGF-β (lower), best predicted who would transition, enabling earlier referral and prevention efforts.
Read MoreThis review highlights the need for predictive biomakers in SLE. A soluble mediator score showed high predictivity of impending flare in diverse population, offering a promising tool for early flare detection and intervention
Read MoreThis study refined the flare risk index using immune mediators profiles in lupus patients. A subset of 11 mediators predicted risk of imminent flare with high sensitivity and specificity, enabling early intervention and improved disease management.
Read MoreThis study assessed baseline and follow-up plasma levels of 52 soluble mediators in African American SLE patients. Results showed 31 elevated Immune mediator preceding disease flare, aiding in personalized flare prediction and intervention strategies
Read MoreThis study evaluated the changes in plasma concentrations of soluble mediators that precede clinically defined disease flares. Findings showed that shift in inflammatory and regualatory cytokines precedes lupus flares, suggesting that soluble mediators can serve as predictive biomakers, enabling earlier intervention and more precise monitoring of disease activity.
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