Cerebral blood flow and cognitive functioning in patients undergoing transcatheter aortic valve implantation.

Background: Approximately one-third of patients with symptomatic severe aortic valve stenosis scheduled for transcatheter aortic valve implantation (TAVI) have some degree of cognitive impairment. The effect of TAVI on cardiac output, cerebral blood flow (CBF), and cognitive functioning has not been systematically studied.

Methods: CAPITA (NCT05481008) is a prospective longitudinal study assessing cerebral and cognitive outcomes in patients that underwent TAVI between August 2020 and October 2022.

Phylogenetic and biochemical drivers of plant species variation in organic compound hydrogen stable isotopes: novel mechanistic constraints.

Significant variation in plant organic compound hydrogen stable isotope (δH) values among species from a single location suggests species biochemistry diversity as a key driver. However, the biochemical mechanisms and the biological relevance behind this species-specific δH variation remain unclear. We analyzed δH values of cellulose and n-alkanes across 179 eudicot species in a botanical garden sampled in 2019, and cellulose, n-alkanes, fatty acids and phytol δH values from 56 eudicot species sampled in 2020.

Deferral of routine percutaneous coronary intervention in patients undergoing transcatheter aortic valve implantation: Rationale and design of the PRO-TAVI trial.

Background: Concomitant coronary artery disease (CAD) is highly prevalent in patients with severe aortic stenosis undergoing transcatheter aortic valve implantation (TAVI). The optimal treatment strategy for CAD is a topic of debate. An initial conservative strategy for CAD in patients undergoing TAVI may be favorable as multiple studies have failed to show an evident beneficial effect of percutaneous coronary intervention (PCI) on mortality after TAVI.

Toward Versatile Small Object Detection with Temporal-YOLOv8.

Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the signal-to-noise ratio is low. In addition, model development choices, such as the loss function, are typically designed around medium-sized objects.