Childhood-onset systemic lupus erythematosus (cSLE) with lupus nephritis (LN) presents therapeutic challenges due to complex mycophenolic acid (MPA) pharmacokinetics. This study developed machine learning (ML)-based models to predict MPA area under the concentration-time curve (MPA-AUC) across three scenarios: determining therapeutic window attainment, precise AUC estimation and forecasting post-dose-adjustment exposure.

FMF is a genetic autoinflammatory condition marked by recurrent fever and serositis. Colchicine is the mainstay treatment. However, 5% to 10% of patients exhibit colchicine resistance, requiring alternative therapies. Early identification of resistance is vital. While some scoring systems exist for paediatric FMF, artificial intelligence's (AI's) potential to predict colchicine resistance in adult patients with FMF has not been systematically explored. This study aimed to utilize machine-learning (ML) and deep-learning (DL) algorithms to predict colchicine resistance in adult patients with FMF.

Electronic health records (EHRs) contain a wealth of unstructured patient data that can be leveraged using artificial intelligence (AI). This study aimed to develop a natural language processing (NLP) pipeline to identify clinical phenotypes and disease trajectories in patients with systemic lupus erythematosus (SLE) from EHRs.

The Absent in Melanoma 2 (AIM2) inflammasome is a crucial producer of IL-1β and IL-18 upon sensing double-stranded (ds)DNA but its role in SLE remains ambiguous. The study aims to investigate the involvement and underlying mechanism leading to AIM2 inflammasome dysregulation in lupus patients.

To assess the role of comorbidities measurements in interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA).

The Polymyalgia Rheumatica Activity Score (PMR-AS) is the best validated composite disease activity measure in PMR to date, but its reliability, measurement error and discriminative validity have not been studied.

With new insights into damage accrual, new outcome measures and new therapies emerging, treatment for lupus nephritis (LN) has evolved over the last years. Although a greater proportion of patients shows clinical responses, treatment reduction and withdrawal remain challenging. While immunosuppressive therapy has relevant side effects, relapses pose the risk of long-term kidney function impairment. Unlike other autoimmune kidney diseases, LN lacks a unique biomarker or biomarker profile clearly reflecting disease activity. Here, we review definitions of remission, LN immunosuppressant withdrawal studies and new biomarkers correlated with disease activity. These factors can help to identify patients who can be safely withdrawn from immunosuppression reducing risk of infection, cardiovascular side effects, toxicity and damage accrual.

Sleep disturbances significantly impact quality of life (QoL) in systemic lupus erythematosus (SLE). This study aimed to assess sleep quality in cases and controls using both subjective and objective measures and explore association with disease characteristics, psychological factors, physical function and QoL.

Pain in PsA is common and multifactorial, involving nociceptive, neuropathic and nociplastic mechanisms. We hypothesized that PsA patients with entheseal pain exhibit distinct brain activation patterns on functional MRI (fMRI), depending on the presence of US-detected inflammation.

Digital vasculopathy (a spectrum of Raynaud's phenomenon, digital ulceration and critical ischaemia) is one of the most characteristic manifestations of systemic sclerosis (SSc). It is an area of unmet need with a major impact on quality of life: current treatments are only poorly effective. SSc-related digital vasculopathy is a result of structural as well as functional change at the level of both the microcirculation and the digital artery, explaining its severity. This review begins with a brief description of digital vasculopathy, followed by its assessment in the clinical setting, relevant to both diagnosis and monitoring of SSc. Outcome measures of Raynaud's phenomenon and of digital ulcers are then discussed, focusing on recent advances. These outcome measures are a 'hot topic' because reliable patient-reported and laboratory-based outcome measures will facilitate much needed clinical trials. Finally, some of the emerging non-invasive technologies which are providing new insights into pathophysiology are briefly described.

SSc is a rare rheumatological disease associated with significant morbidity and mortality. Despite significant recent international clinical trial activity, the yield of approved compounds has been disappointingly low. Our aim was to identify and prioritize potential 'druggable' targets with insights from human genetics, by integrating the available evidence with publicly available bioinformatics sources relevant for SSc drug development.

Systemic autoimmune and chronic inflammatory rheumatic diseases predominantly affect childbearing women. These women are at greater risk for pregnancy complications stemming from both the underlying disease and the treatments required to manage it. This cross-sectional study aimed to assess the prevalence of effective contraceptive use across a wide range of diseases including systemic lupus, systemic sclerosis, Sharp syndrome, Sjögren’s disease, rheumatoid arthritis, and spondyloarthritis.

The continuous increase in Artificial Intelligence (AI) applications in various areas of human life has brought about great changes in many sciences, among which is the health sector. ChatGPT and DeepSeek belong to the category of Large Language Models (LLMs) developed by Artificial Intelligence (AI) using supervised and reinforcement learning techniques. The aim of this article is to evaluate the accuracy and consistency of ChatGPT and DeepSeek models in the diagnosis and treatment of two rheumatologic diseases, ankylosing spondylitis (axSpA) and psoriatic arthritis (PsA).