AIMC Topic: Antirheumatic Agents

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Predicting the progression of difficult-to-treat rheumatoid arthritis by a machine learning scoring system, from the FIRST registry.

RMD open
OBJECTIVES: This study aimed to develop and validate a prediction model for the future progression of difficult-to-treat rheumatoid arthritis (D2T RA) and support the precise use of biologic and targeted synthetic disease-modifying antirheumatic drug...

Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...

Identification of biomarkers related to neutrophil extracellular traps and potential therapeutic drugs for rheumatoid arthritis using computational analysis.

European journal of medical research
BACKGROUND: Neutrophil extracellular traps (NETs) derived from neutrophils are implicated in the pathogenesis of rheumatoid arthritis (RA) pathogenicity, though the underlying mechanisms remain unclear.

Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis.

Scientific reports
To identify clinical features that predict the risk of meeting difficult-to-treat (D2T) rheumatoid arthritis (RA) definition in advance. This retrospective analysis included RA patients from the ATTRA registry who initiated biologic (b-) or targeted ...

Data-driven identification of subgroups in early rheumatoid arthritis: mortality and cardiovascular disease in a cohort from western Norway.

RMD open
AIM: To identify subgroups of early rheumatoid arthritis (RA) based on comorbidities and RA manifestations and to investigate their associated risks of cardiovascular events and mortality.

In search of biomarkers for prediction of drug treatment responses in rheumatoid arthritis: Lessons learned and future perspectives.

Autoimmunity reviews
Prompt initiation of effective drug treatment is crucial for controlling inflammation and preventing disease progression in rheumatoid arthritis, the most prevalent systemic rheumatic disease. The growing range of drug therapies over the past three d...

Ligand supplementation restores the cancer therapy efficacy of the antirheumatic drug auranofin from serum inactivation.

Nature communications
Auranofin, an FDA-approved antirheumatic gold drug, has gained ongoing interest in clinical studies for treating advanced or recurrent tumors. However, gold ion's dynamic thiol exchange nature strongly attenuates its bioactivity due to the fast forma...

Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort.

Arthritis research & therapy
BACKGROUND: The variability in treatment response in people with rheumatoid arthritis (RA) warrants the prediction of patients at high risk of treatment failure. Identification of biomarkers linked to clinical remission in RA is currently a challenge...

A longitudinal cohort study uncovers plasma protein biomarkers predating clinical onset and treatment response of rheumatoid arthritis.

Nature communications
Rheumatoid arthritis (RA) is a systemic inflammatory condition posing challenges in identifying biomarkers for onset, severity and treatment responses. Here we investigate the plasma proteome in a longitudinal cohort of 278 RA patients, alongside 60 ...

A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs.

Scientific reports
Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting millions worldwide, leading to inflammation, joint damage, and reduced quality of life. Although biological disease-modifying antirheumatic drugs (bDMARDs) are effective, they are co...