Rheumatology

Artificial Intelligence in Systemic Sclerosis: Clinical applications, challenges, and future directions.

Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality. Current assessment tools, such as the modified Rodnan skin score, pulmonar...

Phenotypic response surface-guided pharmacotherapy for personalized combination treatment in complex diseases.

INTRODUCTION: Optimizing multidrug regimens for complex diseases remains a major challenge in precision medicine because responses are shaped by interpatient heterogeneity, nonlinear drug interactions, and incomplete mechanistic knowledge. Phenotypic Response Surfaces (PRS) have emerged as a promising computational and translational framework for guiding pharmacotherapy by linking phenotypic infor...

Digital experts for fibromyalgia: A comparative analysis of ChatGPT, Gemini, and DeepSeek in addressing patient questions with focus on readability and medical accuracy.

BackgroundPatients with fibromyalgia require clear and reliable medical information to manage a complex chronic condition. AI-based tools may offer va...

Explainable machine learning for predicting infections that require hospitalization in patients with systemic lupus erythematosus.

OBJECTIVES: To develop and validate an explainable artificial intelligence (XAI)-based machine learning (ML) model for predicting infections requiring...

Unraveling defect-mediated ion transport behavior in anti-perovskite solid-state electrolytes via machine learning molecular dynamics simulations.

Anti-perovskite (AP) solid-state electrolytes (SSEs) have emerged as promising candidates for high-safety solid-state batteries due to their wide elec...

Machine learning-based prediction of multi-level antimicrobial resistance in Klebsiella pneumoniae using whole-genome sequencing data.

OBJECTIVES: In this study, we developed an integrated approach for accurate and comprehensive prediction of anti-microbial resistance (AMR) using whol...

Fluid Volumes Longitudinal Modeling to Predict Atrophy and Fibrosis in Neovascular Age-Related Macular Degeneration.

PURPOSE: To evaluate the impact of retinal fluid volumes on the development of atrophy and fibrosis in neovascular age-related macular degeneration (n...

Dataset of manually curated peptides against arthropod-borne viruses.

Anti-arboviral peptides are biomolecules capable of interfering with key stages of the arboviral lifecycle. We present a dataset compiling 270 peptide...

From pathogenesis to precision medicine in systemic lupus erythematosus: Emerging biomarkers and targeted interventions.

Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease characterized by immune dysregulation, autoantibody production, and chron...

Predicting Molecular Laser Properties from First-Principles Using Machine Learning-Based Nuclear Ensemble Approach Spectra.

The accurate prediction of absorption and emission spectra of molecular compounds using quantum mechanical (QM) methods is essential for understanding...

A global perspective on autoimmunity and immunodeficiency: exploring geoepidemiology trends.

Autoimmune and primary immunodeficiency disorders represent a growing global health burden influenced by a complex interplay of genetic, environmental...

Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors.

Generative design and machine learning are increasingly prevalent in medicinal chemistry. To pilot the comprehensive use of automated molecular design...

Using causal machine learning and real world data to improve dose response decision making for secukinumab in psoriatic arthritis.

Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machin...

Surgically Relevant Knee Phenotypes: The Modified Coronal Plane Alignment of the Knee (mCPAK) System - A Deep Learning-based Classification.

BACKGROUND: The coronal plane alignment of the knee (CPAK) classification proposes nine knee phenotypes based on constitutional limb alignment and joi...

Challenges and Controversies in the Diagnosis and Management of Thymic Epithelial Tumors from the Thymic Tumor Subcommittee of the International Association for the Study of Lung Cancer Rare Tumors Committee.

Thymic epithelial tumors (TETs), comprising thymomas, thymic carcinomas, and thymic neuroendocrine neoplasms, are rare prevascular (anterior) mediasti...

A machine-learning-based tool for stroke risk prediction in blunt cerebrovascular injury: development and preliminary evaluation.

PURPOSE: Stroke risk correlates with the Biffl grading system in blunt cerebrovascular injury (BCVI). Although anti-thrombotic therapy is the mainstay...

Vessel Wall Imaging at 7T: State of the Art.

Intracranial vessel wall imaging (VWI) has emerged as a critical tool in neurovascular diagnostics, enabling direct assessment of the vessel wall, whi...

Quinoa bioester application shifts human skin proteome toward molecular profiles associated with younger age.

Skin aging involves complex molecular changes that current strategies struggle to reverse. Here, we developed a machine learning approach using Suppor...

ATEdrug: A reliable human-in-the-loop annotation scheme for aspect term extraction and polarity detection in drug reviews.

Pharmacovigilance is vital for post-market drug safety monitoring. Traditional trials inadequately capture adverse reactions. Patient-generated opinio...

Valorization of turkey cruor through peptic hydrolysis: peptidomic and machine learning approaches for the identification of antimicrobial peptides.

The slaughterhouse blood remains an underexploited by-product with promising potential for producing natural antimicrobial agents. This study investig...