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.

ObjectiveTo explore the role of cucurbitacin B （CuB） in inducing ferroptosis in 4T1 cells and its mechanism. MethodsThe effects of CuB（0.2， 0.4， 0.8 μmol·L^-1）on the proliferation ability of 4T1 cells in vitro were detected using the methyl thiazolyl tetrazolium （MTT） assay. The clonogenic ability of 4T1 cells was detected by the plate cloning assay， and the levels of lactate dehydrogenase （LDH） in 4T1 cells were detected by the use of a kit. The mitochondrial membrane potential and reactive oxygen species （ROS） levels in 4T1 cells were detected by flow cytometry， and the mitochondrial ultrastructure of 4T1 cells was observed by transmission electron microscopy. The western blot was used to detect the expression of ferroptosis-related protein p53 in 4T1 cells， solute carrier family 7 member 11 （SCL7A11）， glutathione peroxidase 4 （GPX4）， long-chain acyl-CoA synthetase 4 （ACSL4）， transferrin receptor protein 1 （TFR1）， and ferritin heavy chain 1 （FTH1）. ResultsCompared with that in the blank group， the survival rate of 4T1 cells in CuB groups was significantly decreased （P<0.05）， and the number of cell clones in CuB groups was significantly reduced （P<0.01）. In addition， compared with that in the blank group， the leakage of LDH in cells in CuB groups was significantly increased （P<0.01）， and the mitochondrial membrane potential of cells in CuB groups decreased significantly （P<0.01）. Cellular ROS levels were significantly elevated in CuB groups （P<0.01）. The mitochondria of cells in CuB groups were obviously wrinkled， and the mitochondrial cristae were reduced or even disappeared. Compared with that in the blank group， the protein expression of p53， ACSL4， and TFR1 were significantly up-regulated in CuB groups （P<0.05）， and that of SLC7A11， GPX4， and FTH1 were significantly down-regulated （P<0.05）. ConclusionCuB may inhibit SLC7A11 and GPX4 expression by up-regulating the expression of p53， which in turn regulates the p53/SLC7A11/GPX4 signaling pathway axis and accelerates the generation of lipid peroxidation substrate by up-regulating the expression of ACSL4. It up-regulates TFR1 expression to promote cellular uptake of Fe³⁺ and down-regulates the expression of FTH1 to reduce the ability of iron storage， resulting in an elevated free Fe²⁺ level. It catalyzes the Fenton reaction， generates excess ROS， imbalances the antioxidant system and iron metabolism， and then induces ferroptosis in 4T1 cells.

ObjectiveTo explore the role of cucurbitacin B （CuB） in inducing ferroptosis in 4T1 cells and its mechanism. MethodsThe effects of CuB（0.2， 0.4， 0.8 μmol·L^-1）on the proliferation ability of 4T1 cells in vitro were detected using the methyl thiazolyl tetrazolium （MTT） assay. The clonogenic ability of 4T1 cells was detected by the plate cloning assay， and the levels of lactate dehydrogenase （LDH） in 4T1 cells were detected by the use of a kit. The mitochondrial membrane potential and reactive oxygen species （ROS） levels in 4T1 cells were detected by flow cytometry， and the mitochondrial ultrastructure of 4T1 cells was observed by transmission electron microscopy. The western blot was used to detect the expression of ferroptosis-related protein p53 in 4T1 cells， solute carrier family 7 member 11 （SCL7A11）， glutathione peroxidase 4 （GPX4）， long-chain acyl-CoA synthetase 4 （ACSL4）， transferrin receptor protein 1 （TFR1）， and ferritin heavy chain 1 （FTH1）. ResultsCompared with that in the blank group， the survival rate of 4T1 cells in CuB groups was significantly decreased （P<0.05）， and the number of cell clones in CuB groups was significantly reduced （P<0.01）. In addition， compared with that in the blank group， the leakage of LDH in cells in CuB groups was significantly increased （P<0.01）， and the mitochondrial membrane potential of cells in CuB groups decreased significantly （P<0.01）. Cellular ROS levels were significantly elevated in CuB groups （P<0.01）. The mitochondria of cells in CuB groups were obviously wrinkled， and the mitochondrial cristae were reduced or even disappeared. Compared with that in the blank group， the protein expression of p53， ACSL4， and TFR1 were significantly up-regulated in CuB groups （P<0.05）， and that of SLC7A11， GPX4， and FTH1 were significantly down-regulated （P<0.05）. ConclusionCuB may inhibit SLC7A11 and GPX4 expression by up-regulating the expression of p53， which in turn regulates the p53/SLC7A11/GPX4 signaling pathway axis and accelerates the generation of lipid peroxidation substrate by up-regulating the expression of ACSL4. It up-regulates TFR1 expression to promote cellular uptake of Fe³⁺ and down-regulates the expression of FTH1 to reduce the ability of iron storage， resulting in an elevated free Fe²⁺ level. It catalyzes the Fenton reaction， generates excess ROS， imbalances the antioxidant system and iron metabolism， and then induces ferroptosis in 4T1 cells.

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 assess the clinical effect of the Yiqi Huoxue Huazhuo therapy(the therapy for replenishing qi,activating blood and resolving turbidity)for the treatment of hepatic fibrosis in Wilson's disease(WD,also known as hepatolenticular degeneration).Methods Using retrospective research method,52 patients with liver fibrosis in WD of qi deficiency and blood stasis type were divided into 24 cases in the control group and 28 cases in the treatment group according to the treatment method.The control group was treated with conventional decopper therapy with western medicines,and the treatment group was treated with Chinese herbal decoction based on Yiqi Huoxue Huazhuo therapy together with conventional decopper therapy.Both groups were treated for a total of 4 weeks.Before and after the treatment,the two groups were observed in the changes of traditional Chinese medicine(TCM)syndrome scores,Unified Wilson's Disease Rating Scale(UWDRS)hepatic symptom scores,serum levels of liver fibrosis indicators of pre-collagen typeⅢ(PCⅢ),hyaluronic acid(HA),collagenⅣ(CⅣ),and laminin(LN),C-X-C motif chemokine ligand 10(CXCL10)level,and the point shear-wave elastography(pSWE)values of hepatic ultrasound based on acoustic radiation force impulse imaging(ARFI).After treatment,the clinical efficacy of the two groups was evaluated.Results(1)After 4 weeks of treatment,the total effective rate of the treatment group was 85.71%(24/28),while that of the control group was 54.17%(13/24),and the intergroup comparison(tested by chi-square test)showed that the therapeutic efficacy of the treatment group was significantly superior to that of the control group(P＜0.05).(2)After treatment,the TCM syndrome scores in both groups were decreased compared with those before treatment(P＜0.01),and the decrease of TCM syndrome scores in the treatment group was significantly superior to that in the control group(P＜0.05).(3)After treatment,the UWDRS liver symptom scores in the two groups were decreased compared with those before treatment(P＜0.01),but the difference was not statistically significant when comparing between the two groups after treatment(P＞0.05).(4)After treatment,serum levels of liver fibrosis indicators of HA,LN,CⅣ and PCⅢ in the treatment group were all decreased compared with those before treatment(P＜0.01),while in the control group only serum LN and PCⅢlevels were decreased(P＜0.05).The intergroup comparison showed that the decrease of serum HA,LN,and PCⅢlevels in the treatment group was superior to that in the control group(P＜0.05 or P＜0.01),while the decrease of serum CⅣlevel tended to be superior to that in the control group,but the difference was not statistically significant(P＞0.05).(5)After treatment,the serum chemokine CXCL10 level in the treatment group was significantly decreased compared with that before treatment(P＜0.01),while the level tended to decrease in the control group,but the difference was not statistically significant(P＞0.05).The intergroup comparison showed that the reduction of serum CXCL10 level in the treatment group was significantly superior to that in the control group(P＜0.05).(6)After treatment,the pSWE values of hepatic ultrasound in the two groups were lower than those before treatment(P＜0.01),and the reduction of pSWE values in treatment group was significantly superior to that of the control group(P＜0.01).Conclusion Yiqi Huoxue Huazhuo therapy can effectively reduce the TCM syndrome scores of WD patients,improve the UWDRS hepatic symptom scores,down-regulate the liver fibrosis indicator level and serum CXCL10 expression level,reduce the pSWE values of hepatic ultrasound,and enhance the clinical efficacy.

Objective:To investigate the impact of the interval period between biopsy and MR examination on tumor detection and extraprostatic extension (EPE) assessment for prostate cancer (PCa) using multi-parametric MRI (mpMRI).Methods:The study was cross-sectional and retrospectively included 130 patients with PCa who underwent RP and preoperative systematic biopsies followed by mpMRI between January 2021 and December 2022 in the First Medical Center of Chinese PLA General Hospital. Patients were divided into 3 groups according to interval following biopsy (group A,<3 weeks, 31 cases; group B, 3-6 weeks, 67 cases; group C,>6 weeks, 32 cases). The percentages of hemorrhage volume in the total prostate were drawn on T 1WI and calculated. The junior, senior and expert radiologists independently localized the index lesions and calculated the accuracy for tumor detection, in addition to assessing the probabilities of EPE according to EPE grade. The correlation between the hemorrhage extent and interval was analyzed using the Spearman correlation coefficient. The accuracy for tumor detection was compared using χ2 test among groups. The diagnostic performance of the radiologists for EPE prediction was assessed using the receiver operating characteristic curve, and the differences between the corresponding area under the curve (AUC) were compared using the DeLong test. Results:The percentage of hemorrhage was correlated with the interval between biopsy and MR examination ( r=-0.325, P<0.001). The detection accuracy of junior radiologist was 83.9% (26/31), 76.1% (51/67), and 78.1% (25/32) in group A, B and C, respectively; no differences were observed in the detection accuracy among three groups ( χ2=0.76, P=0.685). The detection accuracy of senior radiologist was 83.9% (26/31), 80.6% (54/67), and 71.9% (23/32) in 3 groups with no differences ( χ2=1.53, P=0.464). The detection accuracy of expert radiologist was 80.6% (25/31), 77.6% (52/67), and 93.8% (30/32) with no differences ( χ2=3.95, P=0.139). The AUC (95% CI) for predicting EPE were 0.830 (0.652-0.940), 0.704 (0.580-0.809), 0.800 (0.621-0.920) in the group A, B and C for junior radiologist; 0.876 (0.708-0.966), 0.768 (0.659-0.863), 0.896 (0.736-0.975) for senior radiologist; and 0.866 (0.695-0.961), 0.813 (0.699-0.895), 0.852 (0.682-0.952) for expert radiologist, respectively. No differences were observed among the subgroups in each radiologist ( P>0.05). Conclusion:The interval period does not significantly affect the detection accuracy and EPE assessment of PCa using mpMRI. There is probably no necessity for prolonged intervals following systematic biopsy to preserve the clarity of MRI interpretation for PCa.