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This study explored the role of technology systems in influencing nurses' intentions to adopt medical applications that enhance their performance and how technology contributes to improvements in hospital systems. The study examines the intention to use technology through the mediating effects of perceived usefulness and perceived ease of use, with technology sophistication. A random sampling method was employed to gather 687 responses from nurses.

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Background: In Egypt, approximately 10% of preterm deliveries occur between 32 and fewer than 37 weeks, leading to high neonatal intensive care unit (NICU) admissions. Preterm infants often face oral feeding difficulties due to immature development, which can lead to extended hospital stays and increased health risks.

Aim: To assess neonatal nurses' performance in terms of the transition to oral feeding in preterm infants, focusing on knowledge, practices, and attitudes.

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Background: The increasing global and national prevalence of childhood obesity particularly among schoolchildren has warranted a more viable school-based obesity intervention. Apart from physical activity, nutrition is important in any obesity intervention package. This study examined the effects of the MyBFF@school program with nutrition education intervention (NEI) on nutrition knowledge and attitude of overweight and obese secondary schoolchildren.

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Background: The COVID-19 pandemic involved business closures (e.g., gyms), social distancing policies, and prolonged stressful situations that may have impacted engagement in health behaviors.

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The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration.

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