Rheumatology

Vessel Wall Imaging at 7T: State of the Art.

Intracranial vessel wall imaging (VWI) has emerged as a critical tool in neurovascular diagnostics, enabling direct assessment of the vessel wall, which conventional luminal imaging techniques cannot address fully. While commonly available 3T-MRI systems have been valuable for VWI, limitations in spatial resolution and SNR hinder their ability to visualize medium and smaller caliber vessels. Ultra...

Quinoa bioester application shifts human skin proteome toward molecular profiles associated with younger age.

Skin aging involves complex molecular changes that current strategies struggle to reverse. Here, we developed a machine learning approach using Support Vector Regression to predict biological skin age from proteomic profiles, enabling objective assessment of anti-aging formulations. In this exploratory, proof-of-concept, prospective observational study, we set out to characterize treatment-induced...

ATEdrug: A reliable human-in-the-loop annotation scheme for aspect term extraction and polarity detection in drug reviews.

Pharmacovigilance is vital for post-market drug safety monitoring. Traditional trials inadequately capture adverse reactions. Patient-generated opinio...

Valorization of turkey cruor through peptic hydrolysis: peptidomic and machine learning approaches for the identification of antimicrobial peptides.

The slaughterhouse blood remains an underexploited by-product with promising potential for producing natural antimicrobial agents. This study investig...

Fine-grained and multi-pattern anti-nuclear antibody recognition: A new dataset and framework.

Indirect immunofluorescence (IIF) staining imaging on human epithelial cell (HEp-2) is a standard technique for anti-nuclear antibodies (ANA) screenin...

Why we need to maintain a critical view on big data and artificial intelligence predictions.

Artificial intelligence (AI) and machine learning are widely promoted as transformative tools for medical practice, yet their impact in daily rheumato...

Antimicrobial peptides in malaria and tuberculosis management: a systematic review of emerging evidence.

AMPs (Antimicrobial peptides) are small molecules that are crucial components of biological activities, viz wound healing, angiogenesis, antimicrobial...

irAE-GPT: leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets.

BACKGROUND: Large language models (LLMs) have emerged as transformative technologies, revolutionising natural language understanding and generation ac...

A novel predictive model for evaluating thrombus regression in postpartum venous thromboembolism patients.

INTRODUCTION: Antithrombotic evaluation of postpartum venous thromboembolism (VTE) after anticoagulant therapy is challenging because of the lack of h...

Imaging and machine learning reveal pigment ring spatial characteristics in vitiligo progression.

BACKGROUND: Vitiligo is a chronic autoimmune disorder causing skin depigmentation due to melanocyte loss, with pigment ring dynamics being key to unde...

Screening and classification of anti-angiogenic VEGFR2 inhibitors with supervised machine learning, deep learning and molecular docking and molecular dynamics simulation.

Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) is a critical therapeutic target in cancer due to its role in pathological angiogenesis and tum...

Cluster-defined gout subtypes exhibit divergent chronic kidney disease trajectories: a machine learning approach in a Chinese prospective cohort.

OBJECTIVE: To systematically evaluate the contribution of clinical heterogeneity to chronic kidney disease (CKD) progression in gout patients using da...

A deep learning approach to assess transendothelial cell trafficking performance.

Transendothelial migration (TEM), is a complex, multistep process impacted by diseases like autoimmune disorders and cancer. Deciphering aspects of th...

Integrated bioinformatics reveals tubulointerstitial immune microenvironment signatures and machine learning-driven prognostication of clinical treatment response in lupus nephritis.

Lupus nephritis (LN) represents the most severe renal manifestation of systemic lupus erythematosus (SLE), contributing to significant morbidity. Whil...

Analyzing multiple-sclerosis progression: stage-specific biomarker insights via explainable machine learning.

BACKGROUND: Multiple Sclerosis (MS) is a chronic autoimmune disease where early diagnosis from Clinically Isolated Syndrome (CIS) remains challenging....

Anterior midcingulate cortex activity during pain catastrophizing predicts and sequentially tracks reduction in pain interference after cognitive behavioral therapy in fibromyalgia.

Fibromyalgia (FM) is a chronic pain disorder characterized by widespread pain, fatigue, and functional disability. Cognitive behavioral therapy (CBT) ...

Models Integrating Disease Heterogeneity and MPA Exposure Predict Systemic and Renal Efficacy of Mycophenolate in Pediatric Lupus Nephritis.

OBJECTIVES: This study aimed to develop and validate integrated prediction models for pediatric lupus nephritis (LN) treated with a mycophenolate mofe...

Identification of rheumatoid arthritis manganese metabolism-related diagnostic biomarkers through bulk and single-cell RNA sequencing analysis.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized primarily by synovial inflammation, often resulting in progressiv...

Artificial intelligence-enabled flexible surface-enhanced Raman scattering substrate based on silver nanoparticles/polypyrrole/chitosan film for sensitive uric acid detection in saliva, serum and urine.

Selective detection of uric acid (UA), a key biomarker associated with cardiovascular diseases, gout and preeclampsia, is important for preventive hea...

Long term evolutions of hard exudates after anti-VEGF therapy for diabetic macular oedema.

PURPOSE: To evaluate longitudinal change in hard exudate (HEs) volume over 5-years following anti-VEGF treatment for diabetic macular oedema (DMO). ME...