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:To investigate the impact of ginseng alcohol extract(GEE)on improving sleep quality in the aged Drosophila model by regulating the redox balance,and to elucidate its associated mechanism.Methods:Thirty-two male drosophila melanogaster(7-days-old)were randomly selected as young group,while 64 male Drosophila melanogaster flies(35-days-old)were randomly assigned to aged model group(n=32)and GEE group(n=32).The sleep parameters,including total sleep duration,daytime sleep duration,night sleep duration,0-4 h of sleep duration after lights off(ZT0-4 sleep duration),deep sleep duration,sleep episodetimes,sleep fragmentation,and the activity parameters such as the total number of locomotor activity daytime locomotor activity amount and nighttime locomotor activity amount were analyzed using the DAM2 Drosophila behavioral analysis system 7 d after administration.The grouping of the drosophila was as above,and there were 100 drosophila ineach group.The differentially expressed proteins in drosophila brain tissue were screened,identified,and functionally analyzed using two-dimensional fluorescence difference gel electrophoresis(2D-DIGE)and matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF/TOF-MS)proteomic methods.The grouping of the drosophila was as above,and there were 100 drosophila in each group.The activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)and the levels of lipid peroxidation product(MDA)in brain tissue of the drosophila were determined using assay kits.Results:Compared with young group,the total sleep duration daytime sleep duration and night sleep cluration of the drosophila in agaed group were decreased(P<0.05 or P<0.01);and the sleep rhythm amplitude was shortened.Compared with aged group,the total sleep duration and daytime and nighttime sleep durations of the drosphila in GEE group were lengthened(P<0.01).Compared with young group,the ZT0-4 sleep duration deep sleep duration and sleep fragment of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the sleep rhythm amplitude was shortened.Compared with young group,the ZT0-4 sleep duration,deep sleep duration,and single sleep fragment of the drosphila in GEE group were significantly prolonged(P<0.01),and the sleep amplitude was increased.Compared with young group,there was no significant difference in diurnal spontaneous activity or total spontaneous activity of the drosophila in aged group(P>0.05),while the nocturnal spontaneous activity was significantly increased(P<0.05).Compared with aged group,the diurnal spontaneous activity,nocturnal spontaneous activity,and total spontaneous activity of the drosophila in GEE group were significantly decreased(P<0.05 or P<0.01).A total of 47 differentially expressed proteins were selected in the 2D-DIGE electrophoretic mapping.Compared with young group,the expressions of 47 differentially expressed protein sites in aged group were down-regulated mainly including glutathione S-transferase,peroxiredoxin 1 and dihydrolipoic dehydrogenase,which were related to redox balance.Compared with young group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the level of MDA was increased(P<0.01);compared with aged group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosphila in GEE group were increased(P<0.05 or P<0.01),and the MDA level was decreased(P<0.05).Conclusion:GEE has improvement effect on the sleep quality of aged drosophila,and its possible mechanism may be related to upregulating the activities of antioxidant enzymes,inhibiting the accumulation of lipid peroxidation products,and maintaining redox balance.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

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 To observe the effect of specialist team-led walking-cognition dual-task train-ing combined with active self-disclosure on control and balance abilities in elderly patients with acute ischemic stroke(AIS).Methods A total of 90 elderly AIS patients treated in our hospital from January 2022 to January 2024 were enrolled and randomly assigned into the control group and the observation group,with 45 cases in each group.The control group received routine walk-ing training,while the observation group received specialist team-led walking-cognition dual-task training combined with active self-disclosure intervention.Control ability,balance ability,walking ability,cognitive function and psychological status were compared between the two groups.Results After intervention,the scores of Sheikh Trunk Control Scale and Fugl-Meyer Assessment(FMA),and the static balance score,dynamic balance score and total score of Berg Balance Scale(BBS)were significantly increased in both the observation and the control groups(P＜0.05),and all above scores were obviously higher in the former group than the latter one(P＜0.01).The two groups also obtained notably shorter single-and dual-task walking time after intervention,but there were no statistical difference in the single-task walking time in both groups before and after intervention(P＞0.05).After intervention,the observation group had significantly shorter dual-task walking time(22.87±7.36 s vs 27.52±8.71 s,P=0.008)and lower walking time cost of dual task[(11.16±4.07)％vs(25.61±7.82)％,P=0.000]when compared with the control group.After intervention,the scores of Mini-Mental Status Examination were increased,and the scores of Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale were decreased in the two groups(P＜0.05).Conclusion Specialist team-led walking-cognition dual-task training com-bined with active self-disclosure intervention can effectively improve trunk control ability,balance ability,walking ability,cognitive function and psychological state in elderly AIS patients,has cer-tian clinical application value.