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 To assess the accuracy of pN staging prediction in papillary thyroid carcinoma (PTC) using ultrasound radiomics and deep neural networks (DNN). Methods A retrospective analysis was conducted on 375 patients with pathologically confirmed PTC, comprising 261 cases in the training set and 114 in the test set. Staging was categorized as pN0 (no cervical lymph node metastasis), pN1a (central neck lymph node metastasis), and pN1b (lateral neck lymph node metastasis). An ultrasound physician manually segmented the regions of interest (ROIs) for PTC, extracting 1899 radiomic features. Dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) regression. A DNN model for predicting PTC pN staging was developed using the H2O deep learning platform, trained on the training set, and validated on the test set to assess the accuracy of the optimal model. Results A total of 153 patients were in the pN0 stage, 131 patients in the pN1a stage, and 91 patients in the pN1b stage. LASSO regression selected 15 radiomic features for each PTC. The optimal DNN model, constructed using these 15 features, achieved accuracies of 85.82% on the training set and 81.57% on the test set. Conclusion Ultrasound radiomics of PTC demonstrates high accuracy in predicting pN staging and shows potential for automating N staging in patients.

ObjectiveTo investigate the effect and mechanism of cordycepin in inhibiting fat infiltration after rotator cuff injuries in rats by regulating the Wnt/β-catenin signaling pathway， providing a theoretical basis for clinical treatment of rotator cuff injuries. MethodsFifty SPF-grade female SD rats were used in this study， with 10 randomly selected as the blank group. A rotator cuff injury repair model was established by supraspinatus tendon and suprascapular nerve compression. The successfully modeled rats were randomized into model and low-dose （20 mg·kg^-1）， medium-dose （40 mg·kg^-1）， and high-dose （80 mg·kg^-1） cordycepin groups. After 6 weeks of treatment， the gait analysis was performed to assess the limb function in rats. Oil red O staining and Masson staining were employed to observe pathological changes in the muscle tissue. Enzyme-linked immunosorbent assay （ELISA） was used to measure the levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） in the serum. Immunohistochemistry （IHC） was employed to detect the expression of peroxisome proliferator-activated receptor γ （PPARγ） and CCAAT/enhancer-binding protein α （C/EBPα）， which are markers of adipogenesis. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） and Western blot were employed to determine the mRNA and protein levels， respectively， of Wnt3a， Wnt10b， and β-catenin. ResultsCompared with the blank group， the model group showed decreases in stride length and paw print area （P<0.01）， an increase in ratio of wet muscle mass reduction and a decrease in muscle fiber cross-sectional area （P<0.05）， and decreased ratios of fat infiltration area and collagen fiber area （P<0.01）. Additionally， the model group showed elevated levels of IL-1β， IL-6， and TNF-α （P<0.05）， up-regulated protein levels of PPARγ and C/EBPα （P<0.01）， and down-regulated mRNA and protein levels of Wnt3a， Wnt10b， and β-catenin （P<0.05， P<0.01）. Compared with the model group， the low-， medium-， and high-dose cordycepin groups showed increases in stride length and paw print area （P<0.01）， a decrease in ratio of wet muscle mass reduction and an increase in muscle fiber cross-sectional area （P<0.05）， and increases in ratios of fat infiltration area and collagen fiber area （P<0.05， P<0.01）. In addition， cordycepin lowered the serum levels of IL-1β， IL-6， and TNF-α （P<0.05， P<0.01）， down-regulated the protein levels of PPARγ and C/EBPα （P<0.01）， and up-regulated the mRNA and protein levels of Wnt3a， Wnt10b， and β-catenin （P<0.05， P<0.01）. ConclusionCordycepin can improve the limb function， alleviate rotator cuff muscle atrophy， fat infiltration， and fibrosis， and inhibit inflammation in rats by regulating the Wnt/β-catenin signaling pathway.

Objective To construct a predictive model for the survival of transplant kidneys after kidney transplantation. Methods The clinical data of 366 kidney transplant recipients and donors were retrospectively analyzed, and the recipients were divided into low-risk group (n=101), medium-risk group (n=189), and high-risk group (n=76) based on the kidney donor profile index (KDPI). Each group was further divided into Remuzzi score ≤3 group and Remuzzi score >3 group based on time-zero biopsy Remuzzi scores. Kaplan-Meier method was used to analyze the survival of transplant kidneys. Univariate and multivariate Cox regression analyses were performed to identify risk factors affecting long-term survival after kidney transplantation. A predictive model for transplant kidney survival was established and a nomogram was drawn. The predictive performance of the model was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results The median KDPI was 65%, and the median Remuzzi score was 3. The 5-year survival rate of transplant kidneys was 83.5%. Kaplan-Meier survival curves showed that in the KDPI medium-risk and KDPI high-risk groups, the subgroup with lower Remuzzi score had a higher survival rates of transplant kidneys than the subgroup with higher Remuzzi score. Univariate and multivariate Cox regression analyses showed that KDPI, Remuzzi score, and donor’s age were independent risk factors for transplant kidney loss (all P<0.05). The ROC curve showed that the AUC of the nomogram prediction model established based on independent risk factors for the 1, 3 and 5-year survival rates of transplant kidneys were 0.91, 0.93 and 0.94 for the training set, and 0.89, 0.85 and 0.88 for the validation set. Calibration curves shows good consistency between the training and validation sets of the model. Conclusions The nomogram predictive model based on KDPI, time-zero biopsy Remuzzi score and donor’s age has good predictive value for transplant kidney survival.

Objective To establish an HPLC method to determine the concentration of inositol nicotinate（IN） in rat skin, and study the pharmacokinetic characteristics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats. Methods HPLC method was used to establish a simple and rapid analytical method for the determination of IN concentration in the skin of rats at different time points after administration. The established method was used to study the pharmacokinetics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats, and the pharmacokinetic parameters were fitted with DAS software. Results The linearity of the analytical method was good in the concentration range of 0.25-20 μg/ml, the quantitative limit was 0.25 μg/ml, and the average recovery rate was 96.18%. The pharmacokinetic parameters of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats were as follows: t_1/2 was （4.555±2.054） h, T_max was （6±0）h, C_max was （16.929±2.153）mg/L, AUC_0−t was （150.665±16.568） mg·h /L ,AUC_0−∞ was （161.074±23.917） mg·h /L, MRT_（0−t） was （9.044±0.618）h, MRT_{（0−∞）} was （10.444±1.91） h, CLz/F was （0.19±0.03） L/（h·kg）, and Vz/F was （1.19±0.437） L/（h·kg）. Conclusion IN could quickly penetrate the skin and accumulate in the skin for a long time, which was beneficial to the pharmacological action of drugs on the lesion site for a long time. The method is simple, rapid, specific and reproducible, which could be successfully applied to the pharmacokinetic study of IN after transdermal administration in rats.

[Objective] To investigate the infection and characteristics of hepatitis E virus among blood donors in Hangzhou. [Methods] A total of 5 075 blood samples of blood donors from Zhejiang Provincial Blood Center from September to November 2023 were collected, including 5 037 samples with normal ALT and 38 samples with elevated ALT (>50 U/L). Enzyme-linked immunosorbent assay (ELISA) was used to detect anti-HEV IgM, anti-HEV IgG and HEV-Ag. The Fisher test and Chi-square test were used to evaluate the difference in the reactivity rates of anti-HEV IgM and anti-HEV IgG among different levels of ALT. The distribution characteristics of HEV screening in blood donors were analyzed. Univariate and multivariate logistic regression were used to analyze the susceptibility factors of anti-HEV IgM and anti-HEV IgG seropositivity, and the anti-HEV IgM-reactive blood donors were followed up by telephone. [Results] The reactivity rates of anti-HEV IgM, anti-HEV IgG and HEV-Ag in 5 075 blood samples were 0.45%, 22.98% and 0%, respectively. There was no difference in the reactivity rates of anti-HEV IgM and anti-HEV IgG among different levels of ALT (P>0.05), and the results of univariate and multivariate logistic regression analysis showed that age was a risk factor for anti-HEV IgM and anti-HEV IgG reactivity in blood donors (P<0.05), while no difference in the reactivity rates of anti-HEV IgM and anti-HEV IgG among blood donors was noticed in gender, occupation and education level (P>0.05). [Conclusion] There is a potential risk of transfusion-transmitted HEV (TT-HEV) in Hangzhou, and a cost-effective HEV screening strategy needs to be established to continue regular HEV surveillance in Hangzhou to assess the risk of infection.

Objective: Miniscrews are commonly utilized as temporary anchorage devices (TADs) in cases of maxillary protrusion and premolar extraction. This study aimed to investigate the effects and potential side effects of two conventional miniscrew configurations on the maxillary incisors. Methods: Eighty-two adult patients with maxillary dentoalveolar protrusion who had undergone bilateral first premolar extraction were retrospectively divided into three groups: non-TAD, two posterior miniscrews only (P-TADs), and two anterior and two posterior miniscrews combined (AP-TADs). Cone-beam computed tomography was used to evaluate the maxillary central incisors (U1). Results: The APTADs group had significantly greater U1 intrusion (1.99 ± 2.37 mm, n = 50) and less retroclination (1.70° ± 8.80°) compared to the P-TADs (–0.07 ± 1.65 mm and 9.45° ± 10.68°, n = 60) and non-TAD group (0.30 ± 1.61 mm and 1.91° ± 9.39°, n = 54).However, the AP-TADs group suffered from significantly greater apical root resorption (ARR) of U1 (2.69 ± 1.38 mm) than the P-TADs (1.63 ± 1.46 mm) and non-TAD group (0.89 ± 0.97 mm). Notably, the incidence of grade IV ARR was 16.6% in the AP-TADs group, significantly higher than the rates observed in the P-TADs (6.7%) and non-TAD (1.9%) groups. Multiple regression analysis revealed that after excluding tooth movement factors, the AP-TADs configuration resulted in an additional 0.5 mm of ARR compared with the P-TADs group. Conclusions In cases of maxillary protrusion and premolar extraction, the use of combined anterior and posterior miniscrews enhances incisor intrusion and minimizes torque loss of the maxillary incisors. However, this approach results in more severe ARR, likely due to the increased apical movement and composite force exerted.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.

ObjectiveTo evaluate the clinical efficacy of Huayu Tongluo Formula （化瘀通络方， HTF） in patients with mid-stage diabetic kidney disease of blood stasis syndrome and explore its potential mechanisms. MethodsA multi-center， randomized， double-blind， placebo-controlled clinical trial was conducted. Ninety patients of mid-stage diabetic kidney disease of blood stasis syndrome were divided into a control group of 46 cases and a treatment group of 44 cases. Both groups received conventional western medicine treatment， the treatment group additionally taking HTF， while the control group taking a placebo of the formula. The treatment was administered once daily for 24 weeks. The primary outcomes included 24-hour urine total protein （24 h-UTP）， serum albumin （Alb）， glycated hemoglobin （HbA1c）， and serum creatinine （Scr）.The secondary outcomes included changes in levels of endothelin-1 （ET-1）， nitric oxide （NO）， vascular endothelial growth factor （VEGF）， and traditional Chinese medicine （TCM） syndrome scores before and after treatment. Clinical efficacy was evaluated based on TCM syndrome scores and overall disease outcomes. Adverse reactions and endpoint events were recorded. ResultsIn the treatment group after treatment， 24 h-UTP， ET-1， and VEGF levels significantly decreased （P＜0.05）， Alb and NO levels significantly increased （P＜0.05）； while the TCM syndrome scores for edema， lumbar pain， numbness of limbs， dark purple lips， dark purple tongue or purpura， and thin， rough pulse all significantly decreased （P＜0.05）. In the control group， no significant changes were observed in any of the indicators after treatment （P＞0.05）.Compared with the control group， the treatment group showed significant reductions in 24 h-UTP， ET-1， and VEGF levels， and increases in Alb and NO levels （P＜0.05）. The TCM syndrome scores for edema， lumbar pain， dark purple tongue or purpura， and thin， rough pulse were all lower in the treatment group than in the control group （P＜0.05）. The total effective rate of TCM syndrome in the treatment group was 59.09% （26/44）， and the overall clinical effective rate was 45.45% （20/44）. In the control group， these rates were 15.22% （7/46） and 8.7% （4/46）， respectively， with the treatment group showing significantly better outcomes （P＜0.05）. A total of 7 adverse events occurred across both groups， with no significant difference （P＞0.05）. No endpoint events occurred during the study. ConclusionOn the basis of conventional treatment of Western medicine， HTF can further reduce urinary protein levels and improve clinical symptoms in patients with mid-stage diabetic kidney disease of blood stasis syndrome. The mechanism may be related to its effects on endothelial function.