Steroid Profiles and Precursor-to-Product Ratios Are Altered in Pregnant Women with Preeclampsia.

Steroid hormone imbalance is associated with the pathogenesis of preeclampsia. However, affected enzymes of steroid metabolism and gene and protein expression in serum and placenta have not been elucidated yet. We aimed to investigate steroid hormone profiles and precursor-to-product ratios in preeclamptic women compared to women with healthy pregnancy (controls) to identify potentially affected steroid hormones and their metabolizing enzymes.

CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.

Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI).

MetSCORE: a molecular metric to evaluate the risk of metabolic syndrome based on serum NMR metabolomics.

Background: Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner.