In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

OBJECTIVE: To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures. METHODS: Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details. RESULTS: Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters. CONCLUSION Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans ; Male ; Female ; Radius Fractures/surgery* ; Middle Aged ; Adult ; Aged ; Aged, 80 and over ; Tomography, X-Ray Computed/methods* ; Retrospective Studies ; Software ; Wrist Fractures

PURPOSE: The study aimed to examine the pattern of motorization and the mortality rate related to road traffic crashes in Zunyi (a city in northern Guizhou province of China) from 2013 to 2022, and to identify the epidemiological characteristics of these crashes with to provide insights that could help improve road safety. METHODS: Data were obtained from the Zunyi traffic management data platform, and the mortality rates were calculated. We deployed various analytical methods, including descriptive analysis, Chi-square test or Fisher's exact test for categorical variables, circular distribution map analysis, and Rayleigh test to characterize the traits of road traffic crashes in the region. RESULTS: During the 10-year study period, 7488 people died due to road traffic accidents, with males accounting for 70.4% and females 29.6% (χ² = 101.97, p < 0.001). The mortality rate increased from 7.80 deaths per 100,000 people in 2013 to 10.70 deaths per 100,000 people in 2016, but then decreased to 9.54 deaths per 100,000 people in 2019. A notable finding was that the death rate per 10,000 vehicles declined from 16.09 deaths per 10,000 vehicles in 2013 to 5.48 deaths per 10,000 vehicles in 2022. The study also found that vulnerable road users represented nearly half (48.76%) of all accident fatalities, and unlicensed or inexperienced driving contributed significantly to the occurrence of road traffic accidents. CONCLUSION Although the number of road traffic accidents in Zunyi has decreased, there are still some critical issues that need to be addressed, particularly for vulnerable road users and unlicensed drivers. Our results highlight the need for targeted interventions to address the specific risk factors of road traffic crashes, particularly those affecting vulnerable road users and drivers without sufficient experience or license.
Humans ; Accidents, Traffic/statistics & numerical data* ; China/epidemiology* ; Male ; Female ; Adult ; Middle Aged ; Aged ; Adolescent ; Young Adult ; Child

OBJECTIVE: To prepare clinical-grade human umbilical cord-derived mesenchymal stem cells (hUC-MSC) with high expression of hematopoietic supporting factors and evaluate their stem cell characteristics. METHODS: Fetal umbilical cord tissues were collected from healthy postpartum women during full-term cesarean section. Wharton's jelly was mechanically separated and hUC-MSCs were obtained by explant culture method and enzyme digestion method in an animal serum-free culture system with addition of human platelet lysate. The phenotypic characteristics of hUC-MSCs obtained by two methods were detected by flow cytometry. The differences in proliferation ability between the two groups of hUC-MSCs were identified through CCK-8 assay and colony forming unit-fibroblast (CFU-F) assay. The differences in multilineage differentiation potential between the two groups of hUC-MSCs were identified through induction of adipogenic, osteogenic, and chondrogenic differentiation. The mRNA expression levels of hematopoietic supporting factors such as SCF, IL-3, CXCL12, VCAM1 and ANGPT1 in the two groups of hUC-MSCs were identified by real-time fluorescence quantiative PCR(RT-qPCR). RESULTS: The results of flow cytometry showed that hUC-MSCs obtained by the two methods both expressed high levels of CD73, CD90 and CD105, while lowly expressed CD31, CD45 and HLA-DR. The results of CCK-8 and CFU-F assay showed that the proliferation ability of hUC-MSCs obtained by explant culture method was better than those obtained by enzyme digestion method. The results of the triple lineage differentiation experiment showed that there was no significant difference in multilineage differentiation potential between the two grous of hUC-MSCs. The results of RT-qPCR showed that the mRNA expression levels of hematopoietic supporting factors SCF, IL-3, CXCL12, VCAM1 and ANGPT1 in hUC-MSCs obtained by explant cultrue method were higher than those obtained by enzyme digestion method. CONCLUSION Clinical-grade hUC-MSCs with high expression levels of hematopoietic supporting factors were successfully cultured in an animal serum-free culture system.
Humans ; Mesenchymal Stem Cells/metabolism* ; Umbilical Cord/cytology* ; Cell Differentiation ; Female ; Cell Proliferation ; Cells, Cultured ; Chemokine CXCL12/metabolism* ; Angiopoietin-1/metabolism* ; Vascular Cell Adhesion Molecule-1/metabolism* ; Stem Cell Factor/metabolism* ; Flow Cytometry ; Pregnancy

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

Objective:To report the preclinical trial results of the application of a domestic high-flow percutaneous left ventricular assist device (pLVAD) in patients with low cardiac output syndrome (LCOS) following cardiac surgery.Methods:Six patients who developed LCOS after direct cardiac surgery were implanted with a domestic high-flow pLVAD. Clinical outcomes, including hemodynamic changes, complications, and survival rates were observed post-implantation.Results:Four patients underwent pLVAD implantation under digital subtraction angiography (DSA) guidance, while two patients had the procedure performed under ultrasound guidance. The implantation process was straightforward, rapid, and uneventful, with no instances of bleeding or arrhythmias. The flow rate at the initiation of pLVAD support was 3.8-5.0 (4.22±0.44)L/min, and the flow rate during pump removal was 1.0-1.3(1.18±0.15)L/min. The duration of pLVAD support was 16.5-165.0(101.3±60.65)h. Hemodynamic parameters showed immediate improvement following pLVAD support: mean arterial pressure increased from (62.67±4.46)mmHg to (80.50±18.96)mmHg (1 mmHg=0.133 kPa, P=0.049), cardiac output increased from (2.45±0.66)L/min to (4.35±1.32)L/min( P=0.01), cardiac index improved from (1.95±0.21)L·min -1·m -2 to (2.77±0.33)L·min -1·m -2( P<0.001), pulmonary artery diastolic pressure decreased from (27.50±1.87) mmHg to(18.33±4.18)mmHg( P=0.001), and left ventricular ejection fraction improved from 0.27±0.04 to 0.37±0.06 ( P=0.004). No visible hemoglobinuria was noted during the support period. No malignant arrhythmias or cerebrovascular complications occurred. One patient required transition to surgical LVAD implantation, while the other five patients had the pLVAD successfully removed and were discharged. Three months later, all six patients were alive, with functional status classified as New York Heart Association (NYHA) Class Ⅰ-Ⅱ. Conclusion:The implantation of a domestic high-flow pLVAD provides a safe and effective therapeutic option for patients with LCOS following cardiac surgery.

Objective:To evaluate the performance of an artificial intelligence-based endoscopic virtual ruler (EVR) for non-invasive measurement of esophageal varices (EV) diameter.Methods:Patients with liver cirrhosis and EV hospitalized at the First Affiliated Hospital of Anhui Medical University between October 2022 and May 2023 were prospectively enrolled. EV diameter was measured using visual estimation, esophageal varix manometer (EVM), and EVR, with procedure times recorded. The intraclass correlation coefficient (ICC) was used to assess the consistency of EV diameter measurement obtained from the three methods, and repeated-measures ANOVA was used to compare differences in time measurements across three methods.Results:The study included 41 patients with liver cirrhosis and EV. Inter-observer ICC for visual estimation was 0.594, versus 0.840 for EVM and 0.884 for EVR. The ICC value between the EV diameters measured by EVR and EVM was higher than that of the visual assessment. The ICC value between EV diameter measurement by EVM and EVR was 0.991. Measurement times differed significantly across methods ( P<0.001): visual estimation 18.6±2.2 s (14.7-23.3 s), EVR 41.5±4.1 s (31.7-50.3 s), and EVM 170.8±26.4 s (129.3-229.3 s). Repeated measures analysis of variance (corrected using Greenhouse-Geisser) revealed significant differences in time across the three measurement methods [ F(1.033, 41.313)=1 233.800, P<0.001]. Subsequent Bonferroni post-hoc tests revealed significant differences in time between all method pairs ( P<0.001). Conclusion:EVR provides rapid, non-invasive EV diameter measurements with excellent agreement to EVM assessment, offering an efficient alternative to conventional techniques.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Objective:This study aimed to validated the diagnostic accuracy of Global Leadership Initiative on Malnutrition(GLIM)criteria for malnutrition in pancreatic cancer patients undergoing pancreaticoduodenectomy and to evaluated its prognostic value for postoperative outcome.Methods:A retrospective analysis was conducted on 230 consecutive pancreatic cancer patients who underwent pancreaticoduodenectomy at the Department of Pancreatobiliary Surgery,Sun Yat-sen University Cancer Center,between January 2018 to January 2024.Patients were stratified into malnutrition group and non-malnutrition group using Nutritional Risk Screening 2002(NRS 2002)and GLIM criteria.Multivariable logistic regression identified independent risk factors for postoperative morbidity.Results:GLIM criteria identified malnutrition in 96 patients(41.7％).Compared with the non-malnourished group,the number of preoperative nutritional support(t=20.038,P<0.001),the number of preoperative enteral nutrition support(t=8.377,P=0.004),the number of preoperative parenteral nutrition support(t=22.302,P<0.001),the number of anemia(t=8.037,P=0.005)and preoperative parenteral nutrition use days(t=-2.898,P=0.009),the difference was statistically significant.There were statistically significant differences in C-reactive protein(t=10.944,P=0.008),NLR(t=-2.523,P=0.012)and PNI(t=-2.397,P=0.017)between the two groups before surgery.Preoperative BMI(t=-4.410,P<0.001)was significantly lower in the malnourished group.The number of postoperative parenteral nutrition days(Z=-2.283,P=0.022)and amino acid supplementation during postoperative hospitalization were significantly higher in the malnourished group(Z=-2.309,P=0.021).The incidence of malnutrition was higher in patients with Clavien-Dindo grade≥Ⅲ(P=0.030)and intra-abdominal infections(P=0.049).Multivariable analysis identified preoperative weight loss(OR=2.154,95％CI:1.158～4.005;P=0.015)and BMI reduction(OR=0.175,95％CI:0.040～0.775;P=0.022)as independent predictors of postoperative complications.Conclusions:The GLIM standard effectively characterize malnutrition status in pancreatic cancer patients after pancreaticoduodenectomy patients and demonstrate superior predictive performance for postoperative morbidity.It has good predictive performance and clinical application value.