Rheumatology

Deep learning-based prediction of autoimmune diseases.

Autoimmune Diseases are a complex group of diseases caused by the immune system mistakenly attacking body tissues. Their etiology involves multiple factors such as genetics, environmental factors, and abnormalities in immune cells, making prediction and treatment challenging. T cells, as a core component of the immune system, play a critical role in the human immune system and have a significant i...

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for chemical and genetic seizures, but current methods for detecting anti-seizure responses are limited by incomplete detection of anti-seizure responses (locomotor assays) or low-throughput (electrophysiology, fluorescence microscopy). To overcome these ...

Risk prediction of functional disability among middle-aged and older adults with arthritis: A nationwide cross-sectional study using interpretable machine learning.

BACKGROUND: Arthritis is a common chronic disease among middle-aged and older adults and is strongly related to functional decline.

Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlan...

Concentration-dependent effects of immunomodulatory cocktails on the generation of leukemia-derived dendritic cells, DC mediated T-cell activation and on-target/off-tumor toxicity.

Acute myeloid leukemia (AML) remains a devastating diagnosis in clear need of therapeutic advances. Both targeted dendritic cells (DC) and particularl...

Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.

The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual s...

Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.

INTRODUCTION: Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a ...

InterDIA: Interpretable prediction of drug-induced autoimmunity through ensemble machine learning approaches.

Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to it...

Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring.

Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advan...

Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.

Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence...

Feature selection enhances peptide binding predictions for TCR-specific interactions.

INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocomp...

Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model.

Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cation...

Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

BACKGROUND: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-inte...

Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning.

Nailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes...

Treatment of neovascular age-related macular degeneration: one year real-life results with intravitreal Brolucizumab.

BACKGROUND: Age-related macular degeneration (AMD) is a prevalent cause of irreversible vision loss worldwide, particularly among the elderly populati...

Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.

BACKGROUND: Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is...

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs i...

Opioid growth factor receptor overexpression exerts anti-hepatocellular carcinoma effects by activating P16 and P21 to inhibit proliferation and migration of HepG2 cells.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the second leading cause of cancer death worldwide [19]. Opio...

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including th...

Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches.

AIMS: Thyroid eye disease (TED) is an autoimmune orbital disorder that diminishes the quality of life (QOL) in affected individuals. Graves' ophthalmo...