Gestational exposure to PM, NO, and sex steroid hormones: Identifying critical windows of exposure in the Rochester UPSIDE Cohort.

Background: Sex steroid hormones are critical for maintaining pregnancy and optimal fetal development. Air pollutants are potential endocrine disruptors that may disturb sex steroidogenesis during pregnancy, potentially leading to adverse health outcomes.

Methods: In the Environmental influences on Child Health Outcomes Understanding Pregnancy Signals and Infant Development pregnancy cohort (Rochester, NY), sex steroid concentrations were collected at study visits in early-, mid-, and late-pregnancy in 299 participants.

Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment.

Aims: Proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected on gated cardiac computer tomography (CT) and whether it provides prognostic significance with artificial intelligence (AI).

Methods And Results: A total of 2016 asymptomatic adults with baseline CAC CT scans from a single site were followed up for MACE for 14 years.

Keratinocyte-derived extracellular vesicles in painful diabetic neuropathy.

Painful diabetic neuropathy (PDN) is a challenging complication of diabetes with patients experiencing a painful and burning sensation in their extremities. Existing treatments provide limited relief without addressing the underlying mechanisms of the disease. PDN involves the gradual degeneration of nerve fibers in the skin.

AI-derived automated quantification of cardiac chambers and myocardium from non-contrast CT: Prediction of major adverse cardiovascular events in asymptomatic subjects.

Background And Aims: The significance of left ventricular mass and chamber volumes from non-contrast computed tomography (CT) for predicting major adverse cardiovascular events (MACE) has not been studied. Our objective was to evaluate the role of artificial intelligence-enabled multi-chamber cardiac volumetry from non-contrast CT for long-term risk stratification in asymptomatic subjects without known coronary artery disease.

Methods: Our study included 2022 asymptomatic individuals (55.