Predicting and Ranking Diabetic Ketoacidosis Risk among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S.

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing.

Rapid LC-MS/MS Evaluation of Collagen and Elastin Crosslinks in Human and Mouse Lung Tissue with a Novel Bioanalytical Surrogate Matrix Approach.

Alterations to post-translational crosslinking modifications in the extracellular matrix (ECM) are known to drive the pathogenesis of fibrotic diseases, including idiopathic pulmonary fibrosis (IPF). Thus, the methodology for measuring crosslinking dynamics is valuable for understanding disease progression. The existing crosslinking analysis sample preparation and liquid chromatography tandem mass spectrometry (LC-MS/MS) methods are typically labor-intensive and time-consuming which limits throughput.

Cognitive benefits of folic acid supplementation during pregnancy track with epigenetic changes at an imprint regulator.

Background: The human ZFP57 gene is a major regulator of imprinted genes, maintaining DNA methylation marks that distinguish parent-of-origin-specific alleles. DNA methylation of the gene itself has shown sensitivity to environmental stimuli, particularly folate status. However, the role of DNA methylation in ZFP57's own regulation has not been fully investigated.