AIMC Topic: Autoantibodies
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A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.
Endocrine
May 29, 2024
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
Apr 26, 2024
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
Dec 14, 2023
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
Oct 12, 2023
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...
Computational model of the full-length TSH receptor.
eLife
Oct 28, 2022
(GPCR)The receptor for TSH receptor (TSHR), a G protein coupled receptor (GPCR), is of particular interest as the primary antigen in autoimmune hyperthyroidism (Graves' disease) caused by stimulating TSHR antibodies. To date, only one domain of the e...
Predicting residues involved in anti-DNA autoantibodies with limited neural networks.
Medical & biological engineering & computing
Mar 18, 2022
Computer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for ...
Application of deep-learning to the seronegative side of the NMO spectrum.
Journal of neurology
Jul 30, 2021
OBJECTIVES: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seroposit...
Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.
Frontiers in immunology
Apr 1, 2021
Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely a...
Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas.
The American journal of pathology
Dec 8, 2020
Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment. Using digital pathology, machine learning algorithms were applied to high-resolution, whole-slide i...
Machine Learning to Quantify Humoral Selection in Human Lupus Tubulointerstitial Inflammation.
Frontiers in immunology
Nov 27, 2020
In human lupus nephritis, tubulointerstitial inflammation (TII) is associated with expansion of B cells expressing anti-vimentin antibodies (AVAs). The mechanism by which AVAs are selected is unclear. Herein, we demonstrate that AVA somatic hypermut...
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