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*

Aromatic traditional Chinese medicine(TCM) has played an important role against epidemics and viruses, and volatile components are the main components that exert the pharmacological effects of aromatic TCM. By screening the related monomer components in aromatic TCM against epidemic and viruses and analyzing and endowing TCM with medicinal properties based on its clinical application and pharmacological research according to the theoretical thinking of TCM, the key technical issues of compatibility of TCM monomer components were solved from a theoretical perspective, providing new ideas and methods for screening raw materials and formulas for the development of new TCM drugs. Based on the conditions of antiviral activity, clinical application foundation, definite therapeutic effect, and high safety, a gradient screening of aromatic TCM was carried out. Firstly, 30 aromatic TCM were screened from anti-epidemic literature and clinical trial formulas, and seven volatile monomers were further screened from them. Then, four monomer components with significant effects, namely patchouli alcohol, carvacrol, p-cymene, and eucalyptol were screened. By adopting the "four-step method for a systematic study of TCM properties", the four monomer components were endowed with medicinal properties, and compatibility and combination studies were conducted to explore the theoretical basis of monomer formulas and form monomer formulas guided by TCM theory. The screening results of volatile monomers in aromatic TCM against viral pneumonia included patchouli alcohol, carvacrol, p-cymene, and eucalyptol. The medicinal properties and compatibility theory of volatile monomer components in TCM were explored. Patchouli alcohol was the main herb, with a cool and pungent nature. It entered the lung meridian to dispel evil Qi and has the effects of aromatization, detoxification, and epidemic prevention. Carvacrol was a minister drug with a cool and pungent taste. It had the effects of aromatizing, moistening, and dissolving the exterior, as well as strengthening the spleen and stomach. p-Cymene was an adjunctive medicine with a mild and pungent nature. It entered the lungs and kidneys and had the effects of aromatic purification, cough relief, and asthma relief. Eucalyptol was also an adjunctive medicine with a pungent and warm taste. It had the functions of aromatic purification, cough relief, phlegm reduction, and pain relief. The combination of the four medicines had the effects of aromatizing, moistening, detoxifying, and epidemic prevention, as well as relieving cough and asthma and strengthening the spleen and stomach. They were used to treat viral pneumonia caused by upper respiratory tract viral infections, with symptoms such as chest tightness, cough, wheezing, fatigue, nasal congestion, runny nose, nausea, and vomiting. This study has laid a literature and theoretical foundation for further drug efficacy verification experiments, compatibility efficacy experiments, and subsequent product development and clinical applications, and it serves as an innovative practice that combines literature research, theoretical research, experimental research, and clinical practice to develop new products.
Drugs, Chinese Herbal/therapeutic use* ; Antiviral Agents/pharmacology* ; Humans ; Pneumonia, Viral/virology* ; Medicine, Chinese Traditional ; Volatile Organic Compounds/pharmacology* ; Animals