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

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 records of Maccabi Healthcare Services, we developed and evaluated five machine learning models to classify patients as at-risk for CD autoimmunity prior to first documented diagnosis or positive serum tissue transglutaminase (tTG-IgA). A train set of highly seropositive (tTG-IgA > 10X ULN) cases (n = 677) with likely CD and controls (n = 176,293) with no evidence of CD autoimmunity was used for model development.

Evaluation of a machine learning tool for the early identification of patients with undiagnosed psoriatic arthritis - A retrospective population-based study.

Background: Psoriatic arthritis (PsA), an immune-mediated chronic inflammatory skin and joint disease, affects approximately 0.27% of the adult population, and 20% of patients with psoriasis. Up to 10% of psoriasis patients are estimated for having undiagnosed PsA.

The Interplay Between Epithelial-Mesenchymal Transition (EMT) and the Thyroid Hormones-αvβ3 Axis in Ovarian Cancer.

Ovarian cancer is a highly metastatic disease. The metastatic potential is enhanced by epithelial to mesenchymal transition (EMT) in which αvβ3 integrin plays a role. Thyroid hormones (L-thyroxine, T4, and 3,5,3'-triiodo-L-thyronine, T3) bind this integrin, and we hypothesized that the thyroid hormone-αvβ3 axis may be involved in EMT activity in ovarian cancer.