AIMC Topic: Autoantibodies

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A machine learning tool for early identification of celiac disease autoimmunity.

Scientific reports
Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical reco...

Use and Comparison of Machine Learning Techniques to Discern the Protein Patterns of Autoantibodies Present in Women with and without Breast Pathology.

Journal of proteome research
Breast cancer (BC) has become a global health problem, ranking first in incidence and fifth in mortality in women around the world. Although there are some diagnostic methods for the disease, these are not sufficiently effective and are invasive. In ...

Analysis of the relationships between interferon-stimulated genes and anti-SSA/Ro 60 antibodies in primary Sjögren's syndrome patients via multiomics and machine learning methods.

International immunopharmacology
BACKGROUND: Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by lymphocyte infiltration of the exocrine glands. Interferon-stimulated genes (ISGs) are often upregulated in patients with pSS, and anti-SSA/Ro 60 a...

Machine learning identifies cytokine signatures of disease severity and autoantibody profiles in systemic lupus erythematosus - a pilot study.

Scientific reports
Disrupted cytokine networks and autoantibodies play an important role in the pathogenesis of systemic lupus erythematosus. However, conflicting reports and non-reproducibility have hindered progress regarding the translational potential of cytokines ...

Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BMC neurology
BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, ...

ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles.

British journal of cancer
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection.

A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.

Endocrine
PURPOSE: This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Cell reports. Medicine
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of mach...

Anti-mutated citrullinated vimentin antibodies are increased in IPF patients.

Respiratory medicine and research
INTRO: An increased prevalence of serum anti-MCV antibody is observed in the serum of patients with idiopathic pulmonary fibrosis (IPF) but the clinical relevance of these antibodies is unknown.

Electroencephalographic abnormalities in children with type 1 diabetes mellitus: a prospective study.

Turkish journal of medical sciences
BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (EEG), their correlation with glutamic acid decarboxylase 65 autoantibody (GAD-ab) in newly diagnosed pediatric type 1 diabetes mellitus (T1DM) patients...