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.

In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

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: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors