Rheumatology

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in systemic lupus erythematosus (SLE) through machine learning, combined with single-cell RNA sequencing (scRNA-seq), to screen and validate related genes. The anal...

An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.

Kawasaki disease (KD) is a syndrome of acute systemic vasculitis commonly observed in children. Due to its unclear pathogenesis and the lack of specific diagnostic markers, it is prone to being confused with other diseases that exhibit similar symptoms, making early and accurate diagnosis challenging. This study aimed to develop an interpretable machine learning (ML) diagnostic model for KD. We co...

TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exp...

Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.

PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as ...

Effects of psychological torture and cybertorture with emerging digital technologies under anti-torture legal obligations in China: A mixed methods research in risks and remedies.

International norms and domestic law-making and decision-making often underestimate the effects of psychological torture and the challenges posed by d...

An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.

OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis...

Machine Learning-Based Identification of Novel Exosome-Derived Metabolic Biomarkers for the Diagnosis of Systemic Lupus Erythematosus and Differentiation of Renal Involvement.

OBJECTIVE: This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus (SLE), identify differential metab...

Review of 2024 publications on the applications of artificial intelligence in rheumatology.

The integration of artificial intelligence (AI) into rheumatology has revolutionized research and clinical practice, offering transformative advanceme...

Evaluating large language models as a supplementary patient information resource on antimalarial use in systemic lupus erythematosus.

ObjectiveTo assess the accuracy, completeness, and reproducibility of Large Language Models (LLMs) (Copilot, GPT-3.5, and GPT-4) on antimalarial use i...

Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8 models.

Knee arthritis is a prevalent joint condition that affects many people worldwide. Early detection and appropriate treatment are essential to slow the ...

Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire.

Leprosy is a dermatoneurological disease and can cause irreversible nerve damage. In addition to being able to mimic different rheumatological, neurol...

DeepTree-AAPred: Binary tree-based deep learning model for anti-angiogenic peptides prediction.

Anti-angiogenic peptides (AAPs) show important potential in tumor therapy by limiting the growth and metastasis of tumor cells. Accurate prediction of...

A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control.

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously d...

Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use o...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment for large B-cell lymphoma (LBCL); however,...

Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.

PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection...

Artificial intelligence versus neurologists: A comparative study on multiple sclerosis expertise.

INTRODUCTION: Multiple sclerosis (MS) is an autoimmune neurodegenerative disease affecting the central nervous system. MS diagnosis is complex, requir...

RAG_MCNNIL6: A Retrieval-Augmented Multi-Window Convolutional Network for Accurate Prediction of IL-6 Inducing Epitopes.

Interleukin-6 (IL-6) is a critical cytokine involved in immune regulation, inflammation, and the pathogenesis of various diseases, including autoimmun...

Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.

OBJECTIVE: Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. ...

Machine learning for the early prediction of long-term cognitive outcome in autoimmune encephalitis.

BACKGROUND AND OBJECTIVE: Autoimmune encephalitis (AE) is an immune-mediated disease. Some patients experience persistent cognitive deficits despite r...