Posts tagged Publication
Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals-The OASIS Study

This 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.

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Altered type II interferon precedes autoantibody accrual and elevated type I interferon activity prior to systemic lupus erythematosus classification

This 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.

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Discerning risk of disease transition in relatives of systemic lupus erythematosus patients utilizing soluble mediators and clinical features

This 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.

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Pro-inflammatory adaptive cytokines and shed tumor necrosis factor receptors are elevated preceding systemic lupus erythematosus disease flare

This 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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