ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

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.

Objective To prepare mouse monoclonal antibodies against the ectodomain of E2 (E2ecto) glycoprotein of Western equine encephalitis virus (WEEV). Methods A prokaryotic expression plasmid pET-28a-WEEV E2ecto was constructed and transformed into BL21 (DE3) competent cells. E2ecto protein was expressed by IPTG induction and presented mainly as inclusion bodies. Then the purified E2ecto protein was prepared by denaturation, renaturation and ultrafiltration. BALB/c mice were immunized with the formulated E2ecto protein using QuickAntibody-Mouse5W as an adjuvant via intramuscular route, boosted once at an interval of 21 days. At 35 days post-immunization, mice with antibody titer above 1×10⁴ were inoculated with E2ecto intraperitoneally, and spleen cells were fused with SP2/0 cells three days later. Hybridoma cells secreting specific monoclonal antibodies were screened by the limited dilution method, and ascites were prepared after intraperitoneal inoculation of hybridoma cells. The subtypes and titers of the antibodies in ascites were assayed by ELISA. The biological activity of the mAb was identified by immunofluorescence assay(IFA) on BHK-21 cells which were transfected with eukaryotic expression plasmid pCAGGS-WEEV-CE3E2E1. The specificity of the antibodies were evaluated with E2ecto proteins from EEEV and VEEV. Results Purified WEEV E2ecto protein was successfully expressed and obtained. Four monoclonal antibodies, 3G6G10, 3D7G2, 3B9E8 and 3D5B7, were prepared, and their subtypes were IgG2c(κ), IgM(κ), IgM(κ) and IgG1(κ), respectively. The titers of ascites antibodies 3G6G10, 3B9E8 and 3D7G2 were 10⁵, and 3D5B7 reached 10⁷. None of the four antibody strains cross-reacted with other encephalitis alphavirus such as VEEV and EEEV. Conclusion Four strains of mouse mAb specifically binding WEEV E2ecto are successfully prepared.
Horses ; Animals ; Mice ; Encephalitis Virus, Western Equine ; Ascites ; Immunosuppressive Agents ; Antibodies, Monoclonal ; Immunoglobulin M

OBJECTIVE To evaluate the effectiveness and safety of Panlongqi tablets in the treatment of fractures based on real-world research. METHODS From September 2021 to September 2023， fracture patients admitted to 33 medical institutions were collected retrospectively. Patients who received conventional treatment were divided into control group （n＝3 750）， and patients who received combination of Panlongqi tablets on the basis of conventional treatment were divided into observation group （n＝ 3 706）. Self-reported indicators of patients were collected through telephone follow-up at 0， 4， 7 and 14 days after treatment. The improvement values of pain score， swelling score and health utility value， as well as effective rate and adverse drug reactions were compared between 2 groups. The propensity matching score （PSM） method was adopted to perform baseline matching on patient’s age， gender， fracture site， fracture severity， surgical type， type of hospital， and other indicators. Statistical analysis was performed on each therapeutic effect indicator. RESULTS After PSM， a total of 6 425 patients were included， of which 3 055 were in the observation group and 3 370 were in the control group. After 14 days of treatment， the observation group showed significant improvement in pain score （4.768 vs. 4.353）， swelling fangshiyuan2008@126.com grading score （2.979 vs. 2.391）， and life quality utility value （0.430 vs. 0.363）， as well as effective rate （87.20% vs.75.99%） compared to the control group （P＜0.05）. The results of subgroup analyses conducted by gender， age， hospital type， and fracture site were consistent with the aforementioned results. In terms of safety， the observation group had no serious adverse reactions， with a total of 29 cases of mild adverse reactions such as dizziness， stomach pain， and allergies， with an incidence rate of 0.78%. CONCLUSIONS Panlongqi tablets combined with conventional treatment are significantly better than conventional treatment in improving pain， swelling， quality of life， and effective rate in patients with fractures， and have good safety.

Objective Based on the PDCA cycle theory,it explores the application mode of scenario simulation teaching method in clinical basic skills training.Methods The 96 students majoring in clinical medicine from the First Af-filiated Hospital of Harbin Medical University in 2019 were selected as the research objects,and the experimental group and the control group were set up respectively,with 48 students in each group,and skills training was car-ried out from March to December 2022.The control group was trained in the traditional way,and the experimental group was trained in the scenario simulation teaching method based on the PDCA cycle.The training effect and training satisfaction of the control group and the experimental group were compared and analyzed.Results In the sur-vey of the training effect of the students in the experimental group,the evaluation results of the basic clinical skills operation were higher than those in the control group,and the difference between the two groups was statistically significant(P＜0.01).In terms of the appropriate and safe operation mode,There was no significant difference be-tween the two groups(P＞0.05).In the training satisfaction survey,students in the experimental group were signifi-cantly more satisfied with the training than those students who are in the control group.The difference was statistical-ly significant(P＜0.01).Conclusion The application of scenario simulation teaching method based on PDCA cycle in clinical basic skills training helps to improve the quality of clinical basic skills training and student satisfaction.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.