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.

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.
Humans ; Muscle, Skeletal ; Electromyography/methods* ; Electroencephalography/methods* ; Brain ; Brain Mapping

Three carboline fluorescent probes F1-F3 were designed and synthesized, based on lead compound JYJ-19, an antifungal compound discovered previously by our group. The antifungal activity in vitro results showed that compound F1 had moderate antifungal activity (MIC₈₀ = 32 μg·mL^-1). The stokes shift of F1 is 70 nm. The fluorescent probe F1 has good optical properties and can be used for fluorescence imaging research. Subcellular localization experiments results showed that F1 was enriched in the mitochondria of fungal cells. The detection of intracellular reactive oxygen species levels shows that JYJ-19 enhances intracellular reactive oxygen species levels. The above results indicated that carboline compounds could exert antifungal effects by acting on fungal mitochondria.

BACKGROUND:High tibial osteotomy results in massive blood loss during the perioperative period.Tranexamic acid can effectively reduce perioperative blood loss.However,the method of tranexamic acid application has not been unified. OBJECTIVE:To investigate the effect and safety of different methods of tranexamic acid on perioperative blood loss in the high tibial osteotomy. METHODS:A total of 160 patients who underwent primary unilateral high tibial osteotomy in the Binzhou Medical University Hospital from January 2019 to December 2021,including 69 males and 91 females,were randomly divided into four groups(n=40 per group).Among them,40 patients were given an intravenous infusion of saline containing 2 g tranexamic acid 10 minutes before tourniquet release(venous group);40 patients were given an intravenous infusion of 1 g tranexamic acid and 1 g tranexamic acid was injected through a drainage tube after the closure of the incision(combined group);40 patients were given 2 g tranexamic acid infusion into drainage tube after the closure of the incision(perfusion group);an additional 40 patients were given an intravenous infusion of the same amount of normal saline(blank group).The general information was compared among the four groups of patients.The hemoglobin,hematocrit,intraoperative blood loss,drainage volume,blood transfusion rate,incision complication,and the incidence of deep vein thrombosis were recorded on days 1,3 and 5 after operation in the four groups.The total blood loss and hidden blood loss were calculated. RESULTS AND CONCLUSION:(1)There was no statistically significant difference in general information among the four groups.(2)No significant difference was found in intraoperative blood loss among the four groups.(3)The maximum decreased values of hemoglobin and hematocrit on days 1,3 and 5 after operation,drainage volume,total blood loss and hidden blood loss were all ranked as the combined group

OBJECTIVE To investigate the multi-target mechanism of action of Poecilobdella manillensis lyophilized powder(SZ) against hyperuricemia(HUA) based on network pharmacology and transcriptomics approaches, combined with animal experiments. METHODS Utilizing Symmap, SwissTargetPrediction, and Pharmmapper databases, the potential active components and corresponding targets of SZ were obtained. Through the Gene Cards and OMIM databases, HUA-related targets were obtained. By taking the intersection mapping, the common targets of SZ and HUA were identified. Cytoscape 3.9.0 software was used to construct a drug component-disease target interaction network, and in combination with the STRING database, a protein interaction network was built and core targets were screened. The DAVID database was used to perform GO biological function annotation and KEGG pathway enrichment analysis on the intersecting targets. A mouse model of HUA was constructed using potassium oxyzate combined with high purine diet, and the effects of SZ on these mice were examined using ELISA and biochemical index detection. qRT-PCR was used to validate the results of RNA-Seq and network pharmacology enrichment analysis. RESULTS Network pharmacological analysis identified 11 major bioactive substances in SZ and 72 potential targets involved in the treatment of hyperuricemia, involving multiple biological processes and different signaling pathways. It was shown that SZ significantly reduced serum uric acid, creatinine and urea nitrogen levels in hyperuricemic mice by inhibiting xanthineoxidase activity. SZ also reduced the levels of URAT1 while increasing the levels of ABCG2. RNA sequencing analysis revealed that there were 112, 536 and 107 differentially expressed genes in the model group vs treated group, control group vs model group and control group vs treated group, respectively. qRT-PCR results indicated that SZ downregulated the expression of genes related to Th17 cell differentiation as well as mRNA of genes on IL-17 and PI3K/Akt signaling pathways. CONCLUSION SZ has therapeutic effects on hyperuricemia. The mechanism of action maybe related to the inhibition of hepatic xanthineoxidase activity, down-regulation of URAT1 levels, up-regulation of ABCG2 levels, affecting the differentiation of Th17 cells and thus the IL-17 signaling pathway, thereby reducing the inflammatory response.