Rheumatology

Development and validation of a machine learning model to predict recurrence in polypoidal choroidal vasculopathy: a multicentre prospective study.

AIM: This study aims to develop and validate machine learning models for predicting recurrence in polypoidal choroidal vasculopathy (PCV) patients using optical coherence tomography (OCT) and OCT angiography (OCTA) biomarkers. METHODS: This multicentre prospective study was conducted at 14 hospitals between June 2019 and December 2023. Patients who achieved remission after anti-vascular endothelia...

Machine learning-based cardiovascular risk prediction in systemic lupus erythematosus: development and internal validation of a prognostic model.

BACKGROUND: Cardiovascular (CV) diseases are the leading cause of mortality in patients with systemic lupus erythematosus (SLE). While traditional risk factors inadequately assess CV risk, SLE-specific determinants remain elusive. We have conducted a study of CV outcomes in a cohort of SLE patients, aiming to facilitate individualized risk assessment. METHODS: The LESLY cohort comprised patients d...

Identification of COL5A1 and TGFBI as key basement membrane genes and their molecular mechanisms in gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is a common complication during pregnancy, but the role of the basement membrane (BM) in GDM is not well understoo...

The melting properties of choline chloride in the representation of deep eutectic system phase diagrams.

Choline chloride (ChCl) is the cornerstone of deep eutectic solvents (DES), yet its melting properties remain an important source of uncertainty in th...

CARS-AMP: A Simple and Efficient Deep Learning Model for Antimicrobial Peptide Prediction.

With the increase in global antibiotic resistance, there is an urgent demand for antibiotic substitutes, for which antimicrobial peptides (AMPs) have ...

Balancing Speed and Sensitivity: Echo-Planar Accelerated MRI for ARIA-H Screening in Anti-Aβ Therapeutics.

The recent advent of anti-amyloid-β monoclonal antibodies has introduced new demands for MRI-based screening of amyloid-related imaging abnormalities,...

Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia.

Fibromyalgia involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturbances. ...

The Role of Artificial Intelligence Algorithms in Challenging Diagnostic Cases - Between Potential and Real Clinical Support.

INTRODUCTION: Extranodal natural killer/T-cell lymphoma, nasal type (ENKTL), is a rare EBV-associated malignancy characterized by destructive tumors i...

The evolving role of artificial intelligence in optimizing treatment and patient selection in diabetic macular edema.

Anti-vascular endothelial growth factor (anti-VEGF) therapy is the mainstay of management for diabetic macular edema (DME), but marked variability in ...

Anti-vascular endothelial growth factor therapy for posterior segment diseases: Progress, challenges, and the road ahead.

The emergence of anti-vascular endothelial growth factor (anti-VEGF) therapy has marked a paradigm shift in the treatment of retinal and choroidal vas...

Markerless gait analysis for children and adolescents with juvenile idiopathic arthritis using a machine learning pipeline.

BACKGROUND: Musculoskeletal diseases, such as Juvenile Idiopathic Arthritis (JIA), can lead to altered joint mobility and changes in gait pattern. Whi...

Precision Medicine in Autoimmune Thyroid Diseases: Decoding Biomarkers, Targeted Therapies and Digital Innovation.

INTRODUCTION: Autoimmune thyroid diseases (AITDs), including Graves' disease and Hashimoto's thyroiditis, arise from genetic, epigenetic, and environm...

Elucidating the anti-colorectal cancer mechanism of Physalis Calyx seu Fructus extract via mass spectrometry-molecular networking and bioinformatics.

Chemical profiling combined with computational analysis highlighted luteolin and chrysoeriol as candidate anti-colorectal cancer (CRC) compounds in Ph...

The needle study: Machine learning as a new method for case-finding in celiac disease.

OBJECTIVES: Despite a well-defined diagnostic work-up, uncertainties persist regarding celiac disease (CeD) detection strategies in the general popula...

Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.

BACKGROUND: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis. Autom...

Validity and applicability of machine learning models for systemic lupus erythematosus diagnosis.

BACKGROUND: The diagnosis of systemic lupus erythematosus (SLE) is clinically complex, and early identification is essential for timely intervention a...

Deep learning discriminates seizures from normal brain oscillations in the electroencephalogram of a rat model of post-traumatic epilepsy.

This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid per...

Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.

Here the current and emerging roles of brain positron emission tomography (PET) in Alzheimer's disease (AD) in the era of anti-amyloid-β antibody ther...

Adaptive neighborhood aggregation graph neural network for early diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease (AD) is crucial for timely intervention but remains challenging due to subtle and heterogeneous brain alteratio...

Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus.

This systematic review evaluated the performance and risk of bias in Systemic Lupus Erythematosus (SLE) disease manifestations and case fatality predi...