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.

Amino acid surfactants (AAS) have excellent properties such as low toxicity,antibacterial,mildness,and corrosion resistance. In recent years,they have been widely applied in the field of daily chemicals,food and pharmaceutical industry. It is pointed out that the structure at molecular level largely affects the physical and chemical properties of AAS materials. In this work,the structures of solid-state N-lauroyl-L-alanine (NLLA) including intermolecular interactions,molecular local dynamics and molecular assembly were investigated by a variety of solid-state nuclear magnetic resonance (SSNMR) techniques. Based on 2D 1H-1H double quantum-single quantum (2D 1H-1H DQ-SQ),1H-1H DQ sideband pattern and molecular simulation,it was found that there was a stable intermolecular hydrogen bond formed between the carboxyl units of two NLLA molecules. Combined with the study of 2D frequency switched Lee—Goldberg heteronuclear correlation (2D 13C-1H FSLG-HETCOR) and 13C T1,another type of hydrogen bond was probed,which existed between amide units of neighboring molecules. In addition,the conformation of alkyl chain ends was investigated. According to 1D 13C{1H}cross polarization/magic angle spinning (CP/MAS) and 2D 13C-1H FSLG-HETCOR spectra,it was revealed that the alkyl-chain ends had both gauche and trans conformations. Moreover,the trans conformation showed two distinct 13C chemical shifts,originated from two NLLA molecular assembly forms. Accordingly,two NLLA molecular assembly structures were suggested which were transoid form and cisoid form. This work provided a SSNMR investigation strategy for characterizing the molecular local structure and dynamics of AAS materials,which helped task of researching and developing materials with improved chemical and physical properties.

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.

Objective To observe the application effect of montelukast sodium combined with terbutaline on cough variant asthma(CVA)in children and its influence on airway remodeling and peripheral blood inflammatory indicators.Methods The children with CVA were randomly classified into control group and treatment group.The control group was given aerosol inhalation of terbutaline(5 mg each time,twice a day),and on the basis of the control group,the treatment group was combined with oral administration of montelukast sodium granules(4 mg each time,once a day,taking before going to bed),and both groups were continuously treated for 3 months.The clinical efficacy,airway cross-section area(AO),airway lumen area(AI),airway wall thickness(T),airway wall area(WA),serum interleukin-5(IL-5),eosinophilic chemotactic factor(Eotaxin),macrophage inflammatory protein-1α(MIP-1α)and T lymphocyte subgroups CD4+,CD8+and CD4+/CD8+were compared between the two groups of children,and the medication safety was assessed.Results Fifty-three cases in control group and 53 cases in treatment group were included.After treatment,the total effective rates in treatment group and control group were 96.23％(51 cases/53 cases)and 83.02％(44 cases/53 cases),respectively(P＜0.05).The AO values in treatment group and control group were(39.42±3.67)and(45.69±4.92)mm2;AI values were(22.36±2.85)and(27.06±3.18)mm2;T values were(1.12±0.28)and(1.44±0.33)mm;WA values were(53.82±4.17)and(60.13±4.66)mm2;serum IL-5 levels were(25.46±5.83)and(41.46±7.64)ng·L-1;Eotaxin levels were(181.24±30.05)and(238.21±39.42)ng·L-1;MIP-1a levels were(15.24±3.67)and(22.43±4.05)ng·L-1;CD4+levels were(37.18±4.06)％and(33.57±3.82)％;CD8+levels were(24.08±3.15)％and(27.31±3.07)％;and CD4+/CD8+levels were 1.54±0.33 and 1.24±0.28,respectively(all P＜0.05).The total incidences of adverse drug reactions in treatment group and control group were 9.43％(5 cases/53 cases)and 3.77％(2 cases/53 cases),respectively(P＞0.05).Conclusion Montelukast sodium combined with terbutaline has an exact efficacy in the treatment of CVA in children,and it can effectively reverse airway remodeling,reduce inflammation level and enhance immune function,and it has good safety.

Objective This study aimed to clarify the intervention effect of salidroside(SAL)on lung injury caused by PM2.5 in mice and illuminate the function of SIRT1-PGC-1ɑ axis. Methods Specific pathogen-free(SPF)grade male C57BL/6 mice were randomly assigned to the following groups:control group,SAL group,PM2.5 group,SAL+PM2.5 group.On the first day,SAL was given by gavage,and on the second day,PM2.5 suspension was given by intratracheal instillation.The whole experiment consist of a total of 10 cycles,lasting 20 days.At the end of treatment,blood samples and lung tissues were collected and analyzed.Observation of pathological changes in lung tissue using inverted microscopy and transmission electron microscopy.The expression of inflammatory,antioxidants,apoptosis,and SIRT1-PGC-1ɑ proteins were detected by Western blotting. Results Exposure to PM2.5 leads to obvious morphological and pathologica changes in the lung of mice.PM2.5 caused a decline in levels of antioxidant-related enzymes and protein expressions of HO-1,Nrf2,SOD2,SIRT1 and PGC-1ɑ,and an increase in the protein expressions of IL-6,IL-1β,Bax,caspase-9 and cleaved caspase-3.However,SAL reversed the aforementioned changes caused by PM2.5 by activating the SIRT1-PGC-1α pathway. Conclusion SAL can activate SIRT1-PGC-1ɑ to ameliorate PM2.5-induced lung injury.