Inflammation research : official journal of the European Histamine Research Society ... [et al.]
Jan 14, 2026
OBJECTIVE: To develop an interpretable prognostic prediction model for autoimmune encephalitis (AE) using immunological indicators and to investigate the potential role of nucleophosmin (NPM1) in disease pathogenesis through multi-omics approaches.
Advanced three-dimensional (3D) tissue models that recapitulate autoimmune disease progression can enable mechanistic studies and accelerate the development of targeted therapies with reduced reliance on systemic immunosuppression. Here, we present a...
Acute Rheumatic Fever and Rheumatic Heart Disease (ARF/RHD) affect over 45 million people globally. ARF/RHD are autoimmune complications following group A streptococcal infections. Current diagnosis of ARF requires thorough medical examination, echoc...
OBJECTIVE: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis (anti-MDA5 + DM) is a unique subtype of idiopathic inflammatory myopathy (IIM) with a poorer prognosis. The immune-metabolic landscape underlying anti-MDA5 + DM patho...
BACKGROUND: Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed t...
The diagnostic delay in celiac disease (CD) is currently a burden for individual and society. Biochemical tests may be used in risk-identification of CD to reduce the diagnostic delay, and we aimed to explore prediction models for CD antibody seropos...
OBJECTIVES: To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.
BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high di...
In our study, we aim to predict the antibody serostatus of patients with suspected autoimmune encephalitis (AE) using machine learning based on pre-contrast T2-weighted MR images acquired at symptom onset. A confirmation of seropositivity is of great...
BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Clas...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.