Rheumatology

Personalized glucose forecasting for people with type 1 diabetes using large language models.

BACKGROUND AND OBJECTIVE: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous ins...

Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial.

INTRODUCTION: Membranous nephropathy is an autoimmune kidney disease and the most common cause of ne...

Detection of antibodies in suspected autoimmune encephalitis diseases using machine learning.

In our study, we aim to predict the antibody serostatus of patients with suspected autoimmune enceph...

Role of eccentricity based topological descriptors to predict anti-HIV drugs attributes with supervised machine learning algorithms.

Chemical graphs are mathematical representations of molecular structures, where atoms are represente...

Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning.

BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant ...

Mechanistic Study of Protein Interaction with Natto Inhibitory Peptides Targeting Xanthine Oxidase: Insights from Machine Learning and Molecular Dynamics Simulations.

Bioactive peptides from food sources offer a safe and biocompatible approach to enzyme inhibition, w...

Thinking Like Sonographers: Human-Centered CNN Models for Gout Diagnosis From Musculoskeletal Ultrasound.

We explore the potential of deep convolutional neural network (CNN) models for differential diagnosi...

Artificial intelligence in anti-obesity drug discovery: unlocking next-generation therapeutics.

Obesity, a multifactorial disease linked to severe health risks, requires innovative treatments beyo...

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.

Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) ...

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.

Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health....

Artificial intelligence challenge of discriminating cutaneous arteritis and polyarteritis nodosa based on hematoxylin-and-eosin images of skin biopsy specimens.

Diseases that develop necrotizing vasculitis of cutaneous muscular arteries include cutaneous arteri...

Posttraumatic Arthritis After Anterior Cruciate Ligament Injury: Machine Learning Comparison Between Surgery and Nonoperative Management.

BACKGROUND: Nonoperative and operative management techniques after anterior cruciate ligament (ACL) ...

Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.

Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for le...

Interpretable machine learning model for predicting depression in middle-aged and elderly Chinese arthritis patients: A nationwide prospective cohort study.

CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.

BACKGROUND: High altitude pulmonary edema (HAPE) poses a significant medical challenge to individual...

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 disc...

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 ...

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 pro...

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-ba...

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 p...

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 ...