Yonsei medical journal
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Yonsei medical journal · Jul 2022
Canagliflozin Ameliorates Nonalcoholic Fatty Liver Disease by Regulating Lipid Metabolism and Inhibiting Inflammation through Induction of Autophagy.
Nonalcoholic fatty liver disease (NAFLD) is closely associated with metabolic diseases, including obesity and diabetes, and has gradually become the most common cause of chronic liver disease. We investigated the effects of sodium glucose cotransporter 2 (SGLT2) inhibitor canagliflozin on NAFLD in high-fat diet (HFD)-induced obese mice and possible underlying mechanisms. ⋯ Our findings suggest that canagliflozin ameliorates the pathogenesis of NAFLD by regulating lipid metabolism and inhibiting inflammation, which may be associated with its promotion of autophagy.
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Yonsei medical journal · Jul 2022
Randomized Controlled TrialEffects of Interleukin-17A on the Early Stages of Arterial Thrombosis in Mice.
Interleukin (IL)-17A has been suggested to play a role in the growth and organization of thrombi. We examined whether IL-17A plays a role in the early stages of thrombosis and whether there are sex differences in the effects of IL-17A. ⋯ IL-17A plays a role in the initial st ages of arterial thrombosis in mice. Coagulation factors and monocyte chemoattractant protein-1 may be associated with IL-17A-mediated thrombosis.
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Yonsei medical journal · Jul 2022
Comparison of Clinical Outcomes for Glycopeptides and Beta-Lactams in Methicillin-Susceptible Staphylococcus Aureus Bloodstream Infections.
This study aimed to provide compelling evidence of anti-staphylococcal beta-lactam use for methicillin-susceptible Staphylococcus aureus bloodstream infection (MSSA BSI). ⋯ Definitive therapy with beta-lactams in patients with MSSA BSI was associated with lower 28-day mortality compared to definitive therapy with glycopeptides.
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Yonsei medical journal · Jul 2022
Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.
The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants. ⋯ We have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.
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Yonsei medical journal · Jul 2022
Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method.
To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods. ⋯ Contemporary healthy Korean children showed different rates of skeletal development than GP standard-BA, and systemic bias should be considered when determining children's skeletal maturation.