Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.

Background And Aims: Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS).

Methods: Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF.

Cerebral hypoperfusion exacerbates vascular dysfunction after traumatic brain injury.

Traumatic brain injuries are extremely common, and although most patients recover from their injuries many TBI patients suffer prolonged symptoms and remain at a higher risk for developing cardiovascular disease and neurodegeneration. Moreover, it remains challenging to identify predictors of poor long-term outcomes. Here, we tested the hypothesis that preexisting cerebrovascular impairment exacerbates metabolic and vascular dysfunction and leads to worse outcomes after TBI.

Maternal use of electronic cigarettes and impact on offspring: a double-hit model.

Endothelial dysfunction is a predictor for cardiovascular disease. Preclinical data suggest longstanding cardiovascular and cerebrovascular dysfunction occurs in offspring with perinatal electronic cigarette (Ecig) exposure. Furthermore, direct use of Ecigs increases reactive oxygen species and impairs cerebrovascular function, but the combined effect of direct use in offspring with a history of perinatal exposure (i.

Adsorbents for hydrogen-bond accepting hazardous chemicals by post-synthetic modification of UiO-66-NH.

Adsorbents for hydrogen-bond accepting chemicals such as organophosphates are developed by post-synthetically modifying UiO-66-NH through two analogous condensation reactions to incorporate hydrogen-bond donating adsorbent groups. When benzaldehydes are employed as coupling partners, the resulting imine-functionalized MOFs show improvements in uptake capacity with increasingly electron-deficient adsorbent groups. By contrast, when the coupling partners are benzoic acids, the resulting amide-functionalized MOFs exhibit improvements in uptake capacity with increasingly electron-rich adsorbent groups.