Publications by authors named "D M D'Agostino"
Very-low-carbohydrate diets (LCHF; <50g/day) have been debated for their potential to lower pre-exercise muscle and liver glycogen stores and metabolic efficiency, risking premature fatigue. It is also hypothesized that carbohydrate ingestion during prolonged exercise delays fatigue by increasing carbohydrate oxidation, thereby sparing muscle glycogen. Leveraging a randomized crossover design, we evaluated performance during strenuous time-to-exhaustion (70%⩒O) tests in trained triathletes following 6-week high-carbohydrate (HCLF, 380g/day) or very-low-carbohydrate (LCHF, 40g/day) diets to determine (i) if adoption of the LCHF diet impairs time-to-exhaustion performance, (ii) whether carbohydrate ingestion (10g/hour) 6-12x lower than current CHO fuelling recommendations during low glycogen availability (>15-hour pre-exercise overnight fast and/or LCHF diet) improves time-to-exhaustion by preventing exercise-induced hypoglycemia (EIH; <3.
View Article and Find Full Text PDFUstekinumab (UST) is an interleukin-12/interleukin-23 receptor antagonist approved for the treatment of Crohn's disease (CD). Only limited real-life data on the long-term outcomes of CD patients treated with UST are available. This study assessed UST's long-term effectiveness and safety in a large population-based cohort of moderate to severe CD patients.
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- Glioblastoma (GBM) is the deadliest brain tumor in adults, and current therapies are largely ineffective, which drives the need for new treatment strategies based on the tumor's metabolic needs, specifically glucose and glutamine.
- A ketogenic metabolic therapy (KMT) approach targets these metabolic pathways by combining dietary changes with specific drugs to limit glycolysis and glutaminolysis, while promoting the use of non-fermentable fuels like ketones and fatty acids.
- The glucose-ketone index (GKI) serves as a biomarker to monitor treatment effectiveness, aiming to create a more hostile environment for tumor growth and improve outcomes in GBM as well as potentially other cancer types reliant on similar metabolic pathways.
Article Synopsis
- - Federated learning (FL) is a method that enhances data privacy in healthcare collaborations, with roots in both engineering and statistics, and a need for better recognition of statistical privacy-preserving algorithms.
- - The study compared seven FL frameworks from both domains, evaluating their performance using logistic regression and Lasso modeling on simulated and real-world data, revealing statistical FL algorithms yield less biased estimates while engineering methods can offer better predictions.
- - The research highlights strengths and weaknesses of both FL methods, suggesting their selection based on specific study needs, and calls for increased awareness and integration of these techniques in future healthcare applications.
This study demonstrates a simple approach to synthesize green Cu particles stabilized by poly(n-vinyl)pyrrolidone (PVP): the latter acts as stabilizer and dispersant, and its presence in solution eliminates the need for an inert atmosphere. Synthetic parameters were tuned to obtain particles with diameters >200 nm, to be human-safe and prevent nano-cytotoxicity. PVP and reductant concentrations, with reaction times, were varied to investigate their effect on colloidal stability, kinetics, and particles size.
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