Bowel Preparation Quality in Patients Using Glucagon-Like Peptide-1 Agonists: A Systematic Review and Meta-Analysis.

Background And Aims: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are now the mainstay treatment for many chronic conditions including diabetes mellitus, obesity, heart failure and chronic kidney disease. GLP-1 RAs may affect bowel preparation for a colonoscopy by modifying intestinal motility and gastric emptying. We conducted a systematic review and meta-analysis to investigate the impact of GLP-1 RA use on bowel preparation quality, measured by the Boston Bowel Preparation Scale (BBPS), and the frequency of inadequate preparation events.

Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals.

Introduction: Epilepsy is a neurological disorder in which patients experience recurrent seizures, with the frequency of occurrence more than twice a day, which highly affects a patient's life. In recent years, multiple researchers have proposed multiple machine learning and deep learning-based methods to predict the onset of seizures using electroencephalogram (EEG) signals before they occur; however, robust preprocessing to mitigate the effect of noise, channel selection to reduce dimensionality, and feature extraction remain challenges in accurate prediction.

Methods: This study proposes a novel method for accurately predicting epileptic seizures.

Blood samples collected under anesthesia can be used as a source of non-diseased controls for immune-based assays.

Recruiting very young, healthy children to serve as age-matched controls in research presents substantial ethical and practical challenges. One potential approach to address this issue is to recruit healthy children who are referred for elective procedures under general anesthesia. As infants are typically anesthetized using volatile anesthetics before cannula insertion for additional drug administration, blood samples become readily accessible after the onset of drug-induced coma.

Microgrid anti islanding protection scheme based on deep neural network algorithm and unscented Kalman filtering.

Microgrid anti-islanding protection (MAIP) is an indispensable challenge in ensuring the safe and reliable operation of microgrids. This research article proposes the unscented Kalman filtering (UKF) and deep neural network algorithm (DNN) as an innovative approach to detect and prevent islanding events in microgrids. Initially, the UKF works as a stage-one state observer to analyze the voltage signals at the distributed generation (DG) terminal or point of common coupling (PCC).