AIMC Topic: Arthritis, Rheumatoid

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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 ...

Predicting arthritis risk with machine learning: Insights from the 2023 National Health Interview Survey data.

PloS one
Arthritis, a common chronic disease encompassing multiple subtypes of osteoarthritis and rheumatoid arthritis, was explored in this study as a risk-related factor based on data from the 2023 U.S. National Health Interview Survey (NHIS). The study inc...

Machine Learning Analysis of Cytotoxicity Determinants in Nanoparticle-Based Rheumatoid Arthritis Therapies.

Molecular pharmaceutics
Nanoparticle-based therapies have gained attention in recent years as promising treatments for rheumatoid arthritis (RA), due to the potential offered for targeted delivery, controlled drug release, and improved biocompatibility. A deep understanding...

A DNA methylation-based algorithm for diagnosing rheumatoid arthritis.

Arthritis research & therapy
BACKGROUND: Rheumatoid arthritis (RA), particularly seronegative disease, is difficult to diagnose early, which can delay treatment initiation. This study aims to develop a binary DNA methylation (DNAm)-based algorithm to diagnose RA.

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 ...

Machine learning-based transcriptomic analysis identifies NAMPT and SAT1 as potential biomarkers and therapeutic targets in ferroptosis-associated rheumatoid arthritis.

PloS one
BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disease with chronic presentation, involving symmetric joints and systemic involvement. Ferroptosis is iron-dependent programmed cell death through lipid peroxide accumulation, implicated in infl...

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.

Association between fat-to-muscle ratio and secondary osteoporosis in rheumatoid arthritis: a cross-sectional study at a tertiary hospital in China.

BMJ open
OBJECTIVES: To investigate the correlation between fat-to-muscle ratio (FMR) or other body composition and secondary osteoporosis (OP) in patients with rheumatoid arthritis (RA) and to develop a predictive model using FMR and related clinical factors...