Rheumatology

DeepCR: predicting cytokine receptor proteins through pretrained language models and deep learning networks.

Cytokine receptors play a pivotal role in mediating the immune response and are critical in cytokine storms, which underlie the pathogenesis of conditions such as acute respiratory distress syndrome (ARDS) and autoimmune disorders. Identifying cytokine receptors is essential for understanding their biological functions, exploring therapeutic targets, and guiding clinical interventions. Traditional...

Neuromethods: Basic Techniques for Evaluating the Nervous System in Nonclinical Studies.

The first session of the 2025 European Society of Toxicologic Pathology (ESTP) Congress reviewed routine and specialized methods for microscopic evaluation of neural tissues during nonclinical studies. Three longer presentations reviewed brain sampling approaches in safety assessments, including an example to accentuate topographical analysis and integration of toxicology data; specific brain and ...

Machine Learning-Driven QSAR Modeling of Anti-Cancer Activity from a Rationally Designed Synthetic Flavone Library.

Flavones, recognized as "privileged scaffolds" in drug discovery, hold significant promise as anti-cancer agents. This study aimed to develop a quanti...

A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema.

PURPOSE: To develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME).

Spatial discovery of pyrotinib overcoming HER2-positive breast cancer resistance by breaking fibroblast-induced immune barriers.

BACKGROUND: The TCbHP regimen, consisting of combining docetaxel (T), carboplatin (Cb), trastuzumab (H), and pertuzumab (P), is the preferred neoadjuv...

Serum-Based Assessment of Alopecia Areata Response to Treatment Using ATR-FTIR Spectroscopy.

: Serum diagnostic tests for alopecia areata may be used to monitor response to treatment, aiding in the objective assessment of disease activity and ...

Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States.

BACKGROUND: Juvenile idiopathic arthritis (JIA) is a prevalent chronic rheumatological condition in children, with reported prevalence ranging from 12...

Applying exposure-response analysis to enhance Mycophenolate Mofetil dosing precision in pediatric patients with immune-mediated renal diseases by machine learning models.

BACKGROUND: Mycophenolate mofetil (MMF), a cornerstone immunosuppressant for lupus nephritis, is increasingly used off-label in pediatric immune-media...

Evaluating anti-VEGF responses in diabetic macular edema: A systematic review with AI-powered treatment insights.

Recent advances in deep learning and machine learning have greatly increased the capabilities of extracting features for evaluating the response to an...

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.

Accurate predictions of T cell receptor (TCR) specificity remain an important open problem in immunology, with broad implications for vaccine design, ...

Neurostimulation for the Management of Epilepsy: Advances in Targeted Therapy.

BACKGROUND: Epilepsy is a multifaceted neurological disorder marked by seizures that can present with a wide range of symptoms. Despite the prevalent ...

ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan-Extending Compounds.

Despite the growing interest in developing anti-aging drugs, high costs and low success rates of traditional drug discovery methods pose significant c...

Extrachromosomal circular DNA drives dynamic genome plasticity: emerging roles in disease progression and clinical potential.

Extrachromosomal circular DNA (eccDNA) has emerged as a dynamic and versatile genomic element with key roles in physiological regulation and disease p...

Unmet needs in autoimmune liver diseases.

Autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis are well-defined autoimmune liver diseases, the pathophysiology ...

Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channels.

Droplet impact, spreading and rebound was investigated experimentally on superhydrophobic laser-textured surfaces, yielding a dataset of 1498 datapoin...

Artificial intelligence-driven categorization of rheumatological disease subgroups.

Towards precision medicine in antiphospholipid syndrome.

Antiphospholipid syndrome is a rare systemic autoimmune disorder with complex pathophysiology and high heterogeneity in clinical presentation and trea...

Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study.

OBJECTIVES: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remis...

The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 2. COMI item-content, scoring, language versions, and applications.

In 1998, an international and multidisciplinary group of experts (from the fields of spine surgery, physiotherapy, occupational therapy, rheumatology,...

The Core Outcome Measures Index (COMI) comes of age: 18 years of efficient and effective patient-reported outcome measurement in the field of spinal disorders. Part 1. Development of the COMI, interpretation of scores, and use in research.

In 1998, an international and multidisciplinary group of experts (from the fields of spine surgery, physiotherapy, occupational therapy, rheumatology,...