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http://dx.doi.org/10.1152/japplphysiol.00698.2024 | DOI Listing |
QJM
January 2025
Department of Emergency General Medicine, Mimihara General Hospital.
Ir J Med Sci
January 2025
Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia.
Aim: This study aimed to identify the most commonly used tools by recent pharmacy graduates who successfully passed the Saudi Pharmacists Licensure Examination (SPLE). It also sought to evaluate which tools were perceived as the most useful and representative of the exam content, while considering their monetary value and offering recommendations for future candidates.
Methods: A cross-sectional design was used, involving licensed pharmacists who graduated in 2019 or later and had successfully passed the SPLE.
Arch Microbiol
January 2025
Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia.
Bacteriophages produce endolysins at the end of the lytic cycle, which are crucial for lysing the host cells and releasing virion progeny. This lytic feature allows endolysins to act as effective antimicrobial alternatives when applied exogenously. Staphylococcal endolysins typically possess a modular structure with one or two enzymatically active N-terminal domains (EADs) and a C-terminal cell wall binding domain (CBD).
View Article and Find Full Text PDFPhysiol Rep
February 2025
Department of Biomedical Engineering, Toyo University, Saitama, Japan.
The present study aims to examine the effect of 4 h of continuous sitting on cerebral endothelial function, which is a crucial component of cerebral blood flow regulation. We hypothesized that 4 h of sitting may impair cerebral endothelial function similarly to how it affects lower limb vasculature. Thirteen young, healthy participants were instructed to remain seated for 4 h without moving their lower limbs.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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