Rheumatology

Development and validation of case-finding algorithms for identifying patients with systemic lupus erythematosus in an administrative claim database from tertiary care centers in Japan.

OBJECTIVE: To develop and validate algorithms for identifying patients with systemic lupus erythematosus (SLE) in Japanese administrative claims databases from tertiary care centers using statistical and machine learning methods. METHODS: This retrospective cross-sectional study included 13 538 patients from six hospitals. One-year claims data were linked to chart-confirmed SLE diagnoses. Patients...

Deep Learning-Based Vitiligo Activity Evaluation Using Wood's Lamp Imaging: A Clinical Decision Support.

Vitiligo is an autoimmune disorder characterized by heterogeneous and unpredictable depigmentation, which poses substantial challenges for objective disease monitoring and treatment evaluation in routine clinical practice. To address the lack of automated and quantitative follow-up tools, we designed and validated an end-to-end deep learning-based system using Wood's lamp imaging to support lesion...

Cell-free DNA fragmentomes for noninvasive detection of liver cirrhosis and other diseases.

Accessible liquid biopsies, including analyses of genome-wide cell-free DNA (cfDNA) fragmentation, are emerging for early detection of cancer but rema...

Particle swarm optimized deep learning for jamming detection and throughput enhancement in cognitive radio networks.

Cognitive Radio Networks (CRNs) are essential for improving spectrum efficiency in 5G and beyond; however, their open and adaptive nature makes them h...

GSDME-mediated pyroptosis modulates the immunosuppressive microenvironment in lung adenocarcinoma.

Despite the increasing recognition of pyroptosis, particularly that involving GSDME, its precise impact on tumor prognosis and the immune microenviron...

Deep Learning-Derived Sarcopenia Marker Predicts Benefit from Anti-EGFR Therapy in Patients with RAS Wild-type Metastatic Colorectal Cancer.

PURPOSE: The benefit of treatment intensification in metastatic colorectal cancer (mCRC) may be influenced by host-related factors that are not accoun...

Predicting Non-Response to Multicomponent Treatment in Fibromyalgia: Development and Validation of a Machine Learning Model.

BACKGROUND: Multicomponent programs combining therapeutic exercise, cognitive-behavioral therapy, and pain neuroscience education demonstrate overall ...

Multicolor detection of biothiols: machine learning-empowered plasmonic patterns recognition based on anti-etching of gold nanorods.

Biothiols play pivotal roles in maintaining cellular redox homeostasis, where even minor concentration fluctuations can critically affect physiologica...

Predicting early discontinuation of adalimumab in patients with rheumatoid arthritis using machine learning: A specialty pharmacy-based approach.

BACKGROUND: Patients with rheumatoid arthritis (RA) prescribed adalimumab often discontinue treatment within 6 months because of a perceived lack of b...

Regenerative and Stem Cell-Based Therapies for Arthritis: Harnessing Mesenchymal Stem Cells, Exosomes, and Bioengineered Scaffolds for Functional Joint Repair.

Arthritis, encompassing degenerative disorders such as osteoarthritis (OA) and autoimmune diseases like rheumatoid arthritis (RA), remains a leading c...

Disabilities and Generative Artificial Intelligence Education in Rheumatology Fellowship: Educational Module and Simulation Exercise.

OBJECTIVE: People with rheumatic and musculoskeletal diseases (RMDs) can acquire disabilities that affect their participation in work and school. Gene...

Materiobiology-guided regulation of mesenchymal stromal cell fate for aging-related diseases: From basic parameter design to clinical application.

Mesenchymal stromal cells (MSCs) possess potent immunomodulatory, pro-angiogenic, and regenerative capacities, offering broad clinical promise in rege...

Enhancing the efficacy of Salvia miltiorrhiza and Ligusticum chuanxiong in the treatment of coronary heart disease: The value of poorly soluble components and nanocrystal self-stabilized solid emulsions.

BACKGROUND: The Salvia miltiorrhiza-Ligusticum chuanxiong herb pair is a classic combination in traditional Chinese medicine (TCM) formulation for cor...

Predicting the Course of High-Impact Chronic Pain Using Machine Learning Algorithms.

High-impact chronic pain (HICP) affects over 17 million U.S. adults and follows highly variable courses. To date, the relative importance of biopsycho...

Synergizing Artificial Intelligence with In Vivo CAR-T Cell Generation: A New Paradigm for Democratized Immunotherapy.

In vivo CAR-T cell generation represents a paradigm shift in immunotherapy by transitioning the production of chimeric antigen receptor (CAR) T cells ...

PAST, PRESENT and FUTURE OF LUPUS NEPHRITIS.

INTRODUCTION: Our objective is to delineate the evolution of lupus nephritis (LN) histological classifications and management, from their origins to t...

Salvianolic acid C attenuates liver fibrosis and inhibites hepatic stellate cell activation by targeting FAP/TβRI/SMAD axis.

BACKGROUND: Liver fibrosis remains a major clinical challenge with limited effective therapeutic options. Salvianolic acid C (SAC), a major water-solu...

Artificial Intelligence for Single-Image Diagnosis of Autoimmune Gastritis Using Gastric Corpus Atrophy Images: A Multicenter Retrospective Diagnostic Study.

BACKGROUND AND AIMS: Endoscopic differentiation between autoimmune gastritis (AIG) and Helicobacter pylori-associated atrophic gastritis (HpAG) remain...

Multicentre development and validation of a risk model integrating immunotherapy and coagulation biomarkers for thrombosis in autoimmune neurological disorders.

PURPOSE: Patients with Autoimmune Neurological Disorders (ANDs) require routine immunomodulatory therapy, which inherently increases thrombosis risk. ...

AI-based imputation of anti-Müllerian hormone enables robust prediction of oocyte retrieval during controlled ovarian stimulation.

PURPOSE: This study addressed the practical challenge of missing data in assisted reproductive technology by evaluating the reliability of predicting ...