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.

This study investigated the effects of total flavonoids of Dracocephalum moldavica(TFDM) on apoptosis in rat H9c2 cells induced by endoplasmic reticulum stress(ERS) established by oxygen-glucose deprivation and reoxygenation(OGD/R) injury and tunicamycin(TM), and explored the potential mechanisms. After successful modeling, the following groups were set in this experiment: control group, model(OGD/R or TM) group, and TFDM low-, medium-, and high-dose groups(12.5, 25, and 50 μg·mL~(-1)). The OGD/R injury model was constructed in vitro. Cell proliferation was assessed using the cell counting kit-8(CCK-8) method. The levels of lactate dehydrogenase(LDH) and creatine kinase MB isoenzyme(CKMB) in the cell supernatant were detected. Western blot was used to assess the expression of ERS-related proteins, including glucose regulatory protein 78(GRP78), C/EBP homologous protein(CHOP), activating transcription factor 6(ATF6), and apoptotic proteins B-cell lymphoma 2(Bcl-2) and Bcl-2-associated X protein(Bax). Apoptosis was detected using the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling(TUNEL) method. In the TM-induced ERS model, Western blot was used to measure the expression of ERS pathway-related proteins GRP78, CHOP, inositol-requiring enzyme 1(IRE1), X-box binding protein 1(XBP1), protein kinase RNA-like endoplasmic reticulum kinase(PERK), eukaryotic initiation factor 2α(eIF2α), ATF6, p-ATF6, and apoptotic proteins Bcl-2, Bax, cysteinyl aspartate specific proteinase-12(caspase-12), and cleaved caspase-12. Gene expression of GRP78, CHOP, PERK, and ATF6 was detected by real-time fluorescence quantitative PCR(RT-qPCR). Apoptosis was again detected using the TUNEL method. The results showed that in the OGD/R model, compared with the control group, the levels of LDH and CKMB in the cell supernatant were significantly increased in the OGD/R group. Compared with the OGD/R group, the levels of LDH and CKMB in the TFDM group were significantly reduced. Western blot results revealed that compared with the control group, the expression of ERS-related proteins and Bax in the OGD/R group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the OGD/R group, the expression of ERS-related proteins and Bax in the TFDM groups was significantly reduced, and the expression of Bcl-2 was significantly increased. TUNEL assay showed that apoptosis was significantly decreased after TFDM treatment. In the TM-induced ERS experiment, compared with the control group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TM group was significantly increased, while the expression of Bcl-2 was significantly decreased. Compared with the TM group, the expression of ERS-related genes, ERS-related proteins, and apoptotic proteins in the TFDM group was significantly reduced, and the expression of Bcl-2 was significantly increased. These results suggest that ERS exists in the OGD/R-injured H9c2 cell model, and TFDM can effectively inhibit ERS-induced apoptosis. The mechanism may be related to the downregulation of ERS pathway-related proteins and apoptotic proteins.
Animals ; Endoplasmic Reticulum Stress/drug effects* ; Apoptosis/drug effects* ; Rats ; Flavonoids/pharmacology* ; Glucose/metabolism* ; Cell Line ; Lamiaceae/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Oxygen/metabolism* ; Reperfusion Injury/physiopathology* ; Myocytes, Cardiac/cytology*

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