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The purpose of this study is to enhance the effect of dance skill learning, learning motivation, and physical activity class satisfaction. Moreover, the outcome of these quasi-experiments illustrate the effects of Facebook Live-stream teaching, co-regulated learning (CRL), and experience-based learning (ExBL) on improving students' learning performance. The experimental design in this study was a 2 (CRL vs. non-CRL) × 2 (ExBL vs. non-ExBL) factorial pretest/post-test design. Four classes of a course titled 'Physical Education: Dance' at university level were chosen for this study in one semester. According to the analysis of results, conclusions of this study are that students who receive ExBL have significantly higher physical activity class satisfaction than students who do not receive ExBL. In addition, in the case of ExBL teaching, the concurrent implementation of CRL can improve students' dance skill learning more than ExBL alone.

This study investigated the manufacturing processes for structural supercapacitors (SSCs) using smear molding (RS), resin transfer molding (RTM), and vacuum-assisted resin transfer molding (VARTM). Woven carbon fibers were used as the electrode, woven glass fibers as an insulating layer, and an alkaline/epoxy compound as the electrolyte. In the RTM process, due to the vacuum and the high-pressure injection of the electrolyte, the electrochemical and mechanical properties of the SSC can be greatly improved, and the void contents in the SSC can be reduced. The balanced electrochemical performance and mechanical properties of SSCs were observed in the range of epoxy content from 15 wt% to 30 wt%. This study contributes to the development of SSCs through the establishment of the fabrication process for improvements in part quality. The fabrication method demonstrated here can be directly applied by industries to produce even larger-scale SSCs, opening up new possibilities for practical implementation and scalability. [ABSTRACT FROM AUTHOR]

Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the potential of machine learning techniques for enhancing predictive accuracy has not been fully realized. This study seeks to develop and validate predictive models employing machine learning algorithms to forecast mortality and 30-day post-discharge readmission rates among ICU type 2 diabetes patients, thereby enhancing predictive accuracy and supporting clinical decision-making. Data were extracted and preprocessed from the MIMIC-III database, focusing on 14,222 patients with type 2 diabetes and their corresponding ICU admission records. Comprehensive information, including vital signs, laboratory results, and demographic characteristics, was utilized. Six machine learning algorithms—bagging, AdaBoost, GaussianNB, logistic regression, MLP, and SVC—were developed and evaluated using 10-fold cross-validation to predict mortality at 3 days, 30 days, and 365 days, as well as 30-day post-discharge readmission rates. The machine learning models demonstrated strong predictive performance for both mortality and readmission rates. Notably, the bagging and AdaBoost models showed superior performance in predicting mortality across various time intervals, achieving AUC values up to 0.8112 and an accuracy of 0.8832. In predicting 30-day readmission rates, the MLP and AdaBoost models yielded the highest performance, with AUC values reaching 0.8487 and accuracy rates of 0.9249. The integration of electronic health record data with advanced machine learning techniques significantly enhances the accuracy of mortality and readmission predictions in ICU type 2 diabetes patients. These models facilitate the identification of high-risk patients, enabling timely interventions, improving patient outcomes, and demonstrating the significant potential of machine learning in clinical prediction and decision support. [ABSTRACT FROM AUTHOR]

Background: In the field of postoperative care, infections caused by Gram-positive bacteria pose a major clinical challenge. Vancomycin is a key therapeutic agent whose efficacy is greatly influenced by renal function, particularly by augmented renal clearance (ARC). The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) estimated glomerular filtration rate (eGFR) is an easy and commonly used method to predict ARC; however, it is not well studied to determine vancomycin dose. In this study, we examined the effectiveness of the CKD-EPI equation in determining ARC and optimizing the dose of vancomycin for surgical ward patients. Methodology: A retrospective observational study was conducted to examine 158 surgical ward patients receiving vancomycin. Data on demographics, medical history, and vancomycin dosing were collected. Renal function was evaluated using the CKD-EPI equation, with ARC defined as eGFR ≥ 96.5 mL/min/1.73 m2. Vancomycin pharmacokinetics were calculated using the ClinCalc tool. Results: ARC was in 54% of the patients. Compared with patients without ARC, those with ARC were younger and had lower serum creatinine levels. They also required higher vancomycin doses but had lower trough concentrations and 24-hour area-under-the-curve values. A significant correlation was observed between eGFR and vancomycin clearance, with eGFR > 96.5 mL/min/1.73 m2 necessitating higher vancomycin doses (> 45 mg/kg/day) to achieve the desired area under the curve to minimum inhibitory concentration ratio. Conclusion: For surgical ward patients with CKD-EPI eGFR ≥ 96.5 mL/min/1.73 m2, a vancomycin dosage of > 45 mg/kg/day may be recommended to reach effective therapeutic levels. Overall, this study emphasizes the importance of tailoring vancomycin therapy depending on renal function to ensure efficacy and mitigate the risk of antimicrobial resistance in surgical ward patients. [ABSTRACT FROM AUTHOR]

Background: Researchers have yet to obtain conclusive evidence differentiating among fixed-dose combinations (FDCs) of long-acting muscarinic antagonists (LAMAs) and long-acting beta-agonists (LABAs) for chronic obstructive pulmonary disease (COPD) in terms of real-world clinical outcomes.Research Question: What are the differences between available LAMA/LABA FDCs on the risk of acute exacerbation and cardiovascular events?Study Design and Methods: This retrospective cohort study based on a national insurance claims database included COPD patients aged 40 and older who were newly prescribed glycopyrronium/indacaterol (GLY/IND), umeclidinium/vilanterol (UMEC/VI), or tiotropium/olodaterol (TIO/OLO) FDC between January 1, 2015, and June 30, 2019. Propensity score matching and Cox regression models were used to compare outcomes of acute exacerbation (AE) and cardiovascular events associated with LAMA/LABA FDC treatment.Results: Among the 44,498 patients identified and included, 15,586 received GLY/IND, 20,460 received UMEC/VI, and 8,452 received TIO/OLO. Baseline characteristics were well balanced after 1:1 matching of UMEC/VI and GLY/IND, 2:1 matching of UMEC/VI and TIO/OLO, and 2:1 matching of GLY/IND and TIO/OLO. Risk of severe AE was lower among patients treated with UMEC/VI or GLY/IND than among those who received TIO/OLO (UMEC/VI vs. TIO/OLO: 17.85 vs. 29.32 per 100 person-years; hazard ratio [HR] 0.76, 95% confidence interval [CI] 0.68-0.84; GLY/IND vs. TIO/OLO: 15.54 vs. 25.53 per 100 person-years; HR 0.77, 95% CI 0.67-0.88). In addition, GLY/IND users tended to have a lower risk of cardiovascular events than TIO/OLO users, but the difference dissipated when restricting follow-up to a shorter duration.Interpretation: Our results revealed that the risk of severe AE was lower among COPD patients receiving UMEC/VI or GLY/IND than among those receiving TIO/OLO, while the incidence of cardiovascular events was similar across groups but was slightly lower in GLY/IND users when compared with TIO/OLO users. Further research will be required to confirm these findings. [ABSTRACT FROM AUTHOR]