Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr.
View Article and Find Full Text PDFReceptor tyrosine kinases (RTKs) regulate many cellular functions and are important targets in pharmaceutical development, particularly in cancer treatment. EGFR and EphA2 are two key RTKs that are associated with oncogenic phenotypes. Several studies have reported functional interplay between these receptors, but the mechanism of interaction is still unresolved.
View Article and Find Full Text PDFThe Northern Patagonia coast, characterized by an intricate interaction among terrestrial and marine systems such as Reloncaví Estuarine System (RES), present highly productive marine and aquaculture activities, having a significant socio-economic importance in Chile. Understanding the composition of Organic Matter (OM) in aquatic ecosystems is crucial for elucidating biogeochemical processes, and the use of lipid biomarkers, has proven valuable in identifying OM sources. This study investigates the relationship between phytoplankton biomass indicators, including phytoplankton abundance, chlorophyll-a, and sterol molecules synthesized in high percentages by phytoplankton cells, also known as phytoplankton-derived sterols at the RES.
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