ObjectiveTo investigate the demand and the application outcomes of case-based learning (CBL) combined with teaching case library in continuing education courses for rehabilitation therapists. MethodsA convergent mixed-methods research design was adopted, involving 51 rehabilitation therapists and 31 instructors who participated in the advanced training program at the Department of Rehabilitation Medicine, Peking University Third Hospital between October, 2022 and October, 2024. Self-developed questionnaires were used to collect data on the perceived needs of teachers and students regarding CBL and teaching case library. Differences between CBL + teaching case library and traditional lecturing in student evaluations, classroom participation and interaction were compared using Student Evaluation of Teaching in Medical Lectures, Classroom Participation Scale and Flanders Interaction Analysis System. Semi-structured interviews were conducted to obtain evaluations and attitudes towards this method from both instructors and students' perspectives. ResultsThe survey showed that 91.4% of participating teachers and students supported the use of CBL in the courses, and 82.7% advocated that the teaching case library should include typical cases. Significant differences were observed in teaching preference between teachers and students (χ² = 17.597, P < 0.01). Application effects demonstrated that CBL+teaching library significantly outperformed traditional teaching methods in student previewing behaviors, classroom interaction and learning outcomes (|Z| ≥ 2.646, P < 0.01). Flanders Interaction Analysis indicated that CBL+teaching library was superior to traditional teaching in terms of students' motivation to speak and autonomous learning. Qualitative Research generated four positive themes including cultivating clinical reasoning, being close to clinical practice, deepening knowledge understanding and improving teaching quality; and three negative themes including increasing teaching burden, high software and hardware requirements and posing great challenges to students were generated. ConclusionCompared with traditional teaching methods, CBL combined with teaching case library is closely linked to clinical practice, facilitating students' clinical reasoning, enhancing teaching effectiveness and satisfaction, and therefore aligning with the goals and needs of continuing education for rehabilitation therapists, which is highly recognized by both instructors and students.

ObjectiveTo investigate the demand and the application outcomes of case-based learning (CBL) combined with teaching case library in continuing education courses for rehabilitation therapists. MethodsA convergent mixed-methods research design was adopted, involving 51 rehabilitation therapists and 31 instructors who participated in the advanced training program at the Department of Rehabilitation Medicine, Peking University Third Hospital between October, 2022 and October, 2024. Self-developed questionnaires were used to collect data on the perceived needs of teachers and students regarding CBL and teaching case library. Differences between CBL + teaching case library and traditional lecturing in student evaluations, classroom participation and interaction were compared using Student Evaluation of Teaching in Medical Lectures, Classroom Participation Scale and Flanders Interaction Analysis System. Semi-structured interviews were conducted to obtain evaluations and attitudes towards this method from both instructors and students' perspectives. ResultsThe survey showed that 91.4% of participating teachers and students supported the use of CBL in the courses, and 82.7% advocated that the teaching case library should include typical cases. Significant differences were observed in teaching preference between teachers and students (χ² = 17.597, P < 0.01). Application effects demonstrated that CBL+teaching library significantly outperformed traditional teaching methods in student previewing behaviors, classroom interaction and learning outcomes (|Z| ≥ 2.646, P < 0.01). Flanders Interaction Analysis indicated that CBL+teaching library was superior to traditional teaching in terms of students' motivation to speak and autonomous learning. Qualitative Research generated four positive themes including cultivating clinical reasoning, being close to clinical practice, deepening knowledge understanding and improving teaching quality; and three negative themes including increasing teaching burden, high software and hardware requirements and posing great challenges to students were generated. ConclusionCompared with traditional teaching methods, CBL combined with teaching case library is closely linked to clinical practice, facilitating students' clinical reasoning, enhancing teaching effectiveness and satisfaction, and therefore aligning with the goals and needs of continuing education for rehabilitation therapists, which is highly recognized by both instructors and students.

ObjectiveTo investigate the demand and the application outcomes of case-based learning (CBL) combined with teaching case library in continuing education courses for rehabilitation therapists. MethodsA convergent mixed-methods research design was adopted, involving 51 rehabilitation therapists and 31 instructors who participated in the advanced training program at the Department of Rehabilitation Medicine, Peking University Third Hospital between October, 2022 and October, 2024. Self-developed questionnaires were used to collect data on the perceived needs of teachers and students regarding CBL and teaching case library. Differences between CBL + teaching case library and traditional lecturing in student evaluations, classroom participation and interaction were compared using Student Evaluation of Teaching in Medical Lectures, Classroom Participation Scale and Flanders Interaction Analysis System. Semi-structured interviews were conducted to obtain evaluations and attitudes towards this method from both instructors and students' perspectives. ResultsThe survey showed that 91.4% of participating teachers and students supported the use of CBL in the courses, and 82.7% advocated that the teaching case library should include typical cases. Significant differences were observed in teaching preference between teachers and students (χ² = 17.597, P < 0.01). Application effects demonstrated that CBL+teaching library significantly outperformed traditional teaching methods in student previewing behaviors, classroom interaction and learning outcomes (|Z| ≥ 2.646, P < 0.01). Flanders Interaction Analysis indicated that CBL+teaching library was superior to traditional teaching in terms of students' motivation to speak and autonomous learning. Qualitative Research generated four positive themes including cultivating clinical reasoning, being close to clinical practice, deepening knowledge understanding and improving teaching quality; and three negative themes including increasing teaching burden, high software and hardware requirements and posing great challenges to students were generated. ConclusionCompared with traditional teaching methods, CBL combined with teaching case library is closely linked to clinical practice, facilitating students' clinical reasoning, enhancing teaching effectiveness and satisfaction, and therefore aligning with the goals and needs of continuing education for rehabilitation therapists, which is highly recognized by both instructors and students.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

In this experiment, self-assembled nanoparticles(SANs) were prepared by the pH-driven method, and Her-HCP SAN was constructed by using herpetrione(Her) and Herpetospermum caudigerum polysaccharides(HCPs). The average particle size and polydispersity index(PDI) were used as evaluation indexes for process optimization, and the quality of the final formulation was evaluated in terms of particle size, PDI, Zeta potential, and microstructure. The proposed Her-HCP SAN showed a spheroid structure and uniform morphology, with an average particle size of(244.58±16.84) nm, a PDI of 0.147 1±0.014 8, and a Zeta potential of(-38.52±2.11) mV. Her-HCP SAN significantly increased the saturation solubility of Her by 2.69 times, with a cumulative release of 90.18% within eight hours. The results of in vivo unidirectional intestinal perfusion reveal that Her active pharmaceutical ingredient(API) is most effectively absorbed in the jejunum, where both K_a and P_(app) are significantly higher compared to the ileum(P<0.001). However, the addition of HCP leads to a significant reduction in the P_(app) of Her in the jejunum(P<0.05). Furthermore, the formation of the Her-HCP SAN results in a notably lower P_(app) in the jejunum compared to Her API alone(P<0.001), while both K_a and P_(app) in the ileum are significantly increased(P<0.001, P<0.05). The absorption of Her-HCP SAN at different concentrations in the ileum shows no significant differences, and the pH has no significant effect on the absorption of Her-HCP SAN in the ileum. The addition of the transporter protein inhibitors(indomethacin and rifampicin) significantly increases the absorption parameters K_a and P_(app) of Her-HCP SAN in the ileum(P<0.05,P<0.01), whereas the addition of verapamil has no significant effect on the intestinal absorption parameters of Her-HCP SAN, suggesting that Her may be a substrate for multidrug resistance-associated protein 2 and breast cancer resistance proteins but not a substrate of P-glycoprotein.
Nanoparticles/metabolism* ; Polysaccharides/pharmacokinetics* ; Intestinal Absorption/drug effects* ; Animals ; Rats ; Particle Size ; Drugs, Chinese Herbal/pharmacokinetics* ; Male ; Rats, Sprague-Dawley ; Drug Carriers/chemistry* ; Drug Compounding ; Cucurbitaceae/chemistry*