Effect of Glucagon-Like Peptide-1 Receptor Agonists on Bowel Preparation for Colonoscopy.

Introduction: Inadequate bowel preparation can result in decreased diagnostic accuracy and therapeutic safety of colonoscopy for colon cancer screening. The Boston Bowel Preparation Scale (BBPS) has been used to assess the quality of bowel preparation. Glucagon-like peptide-1 receptor agonists (GLP-1RA) are commonly used medications for diabetes mellitus and obesity that are known to delay gastrointestinal motility.

Structural analysis of a fibre-reinforced composite blade for a 1 MW tidal turbine rotor under degradation of seawater.

This paper presents a structural performance study of a fibre-reinforced composite blade for a 1 MW tidal turbine rotor blade that was designed for a floating tidal turbine device. The 8-m long blade was manufactured by ÉireComposites Teo and its structural performance was experimentally evaluated under mechanical loading in the Large Structures Research Laboratory at the University of Galway. Composite coupons, applied with an accelerated ageing process, were tested to evaluate the influence of seawater ageing effects on the performance of the materials.

The Ross Procedure in Children: Defining the Optimal Age.

Background: It has been proposed that delaying the Ross procedure to later in childhood, allowing autograft stabilisation and placement of a larger pulmonary conduit, may improve outcomes. However, the effect of age at the time of Ross procedure on outcomes remains unclear.

Methods: All patients who underwent the Ross procedure between 1995 and 2018 were included in the study.

Aortic valve repair versus the Ross procedure in children.

Background: Aortic valve repair and the Ross procedure are widely used in children; however, it is unclear which provides the best outcomes.

Methods: Patients who underwent primary aortic valve surgery from 1980 to 2018 were included. Propensity score matching was performed to adjust for baseline differences.

Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset.

Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head.