Conformation and binding of 12 Microcystin (MC) congeners to PPP1 using molecular dynamics simulations: A potential approach in support of an improved MC risk assessment.

Microcystins (MCs) occur frequently during cyanobacterial blooms worldwide, representing a group of currently about 300 known MC congeners, which are structurally highly similar. Human exposure to MCs via contaminated water, food or dietary supplements can lead to severe intoxications with ensuing high morbidity and in some cases mortality. Currently, one MC congener (MC-LR) is almost exclusively considered for risk assessment (RA) by the WHO.

Weight discrimination partially mediates the longitudinal relationship between Body Mass Index and pain.

Pain is common among individuals with high Body Mass Index (BMI). This study investigated weight discrimination as a mediator of the longitudinal relationship between BMI and the presence of moderate/severe pain among adults from the English Longitudinal Study of Ageing (ELSA) cohort. ELSA is a longitudinal study of middle-aged and older adults living in England.

Quality Improvement Project: Implementing a Personalized Professional Growth Pathway in a Nurse Residency Program.

Implementing a personalized professional growth pathway in a nurse residency program at a Midwest health system aimed to enhance newly licensed nurse satisfaction and retention. Replacing the traditional evidence-based practice project with the "Professional Growth Pathway" tailored to nurses' interests and career goals resulted in a 93% retention rate at program completion, surpassing the national average. This innovative approach fosters individual development, aiding recruitment and retention efforts amidst staffing challenges.

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

Objective: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, periprocedural mortality, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of this study was to construct and validate a random forest machine learning tool to predict two year survival following elective AAA repair.

Artificial intelligence and large language models in palliative medicine clinical practice and education.

As we approach 2034, we anticipate significant advancements in digital technologies and their impact across various domains, including palliative and end-of-life care and perhaps higher education more generally. Predicting technological breakthroughs, especially in the realm of artificial intelligence (AI), is notoriously difficult. In a sense, you might need an AI to do this effectively.