Heart failure (HF) increases the risk of developing atrial fibrillation (AF), leading to increased morbidity and mortality. Therefore, better prediction of this risk may improve treatment strategies. Although several predictors based on clinical data have been developed, the establishment of a transcriptome-based predictor of AF incidence in HF has proven to be more problematic.
View Article and Find Full Text PDFFoot-and-mouth disease poses a significant threat to both domestic and wild cloven-hoofed animals, leading to severe economic losses and jeopardizing food security. While machine learning models have become essential for predicting foot-and-mouth disease outbreaks, their effectiveness is often compromised by distribution shifts between training and target datasets, especially in non-stationary environments. Despite the critical impact of these shifts, their implications in foot-and-mouth disease outbreak prediction have been largely overlooked.
View Article and Find Full Text PDFThis study investigated the structural and environmental recovery of weathered hydrocarbon-contaminated soils using low-carbon solutions and aimed to ascertain the suitability of the remediated soils for engineering purposes. 25% (/) of ground ripe (RPP) and unripe (UPP) waste plantain peels were each added to 1 kg weathered hydrocarbon-contaminated soil samples and monitored for 90 days. Biological, physicochemical, and engineering properties were analysed for all samples in triplicates.
View Article and Find Full Text PDFWell-trained, competent therapists are crucial for safe and effective psychedelic-assisted therapy (PAT). The question whether PAT training programs should require aspiring therapists to undergo their own PAT-commonly referred to as "experiential training"-has received much attention within the field. In this article, we analyze the potential benefits of experiential training in PAT by applying the framework developed by Rolf Sandell et al.
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