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.

In 2040, neurodegenerative diseases (NDD) will overtake cancer as the second leading cause of death after cardiovascular and cerebrovascular diseases. Therefore, the search for effective intervention measures has become the top priority to deal with this difficult burden. Hyperbaric oxygen therapy (HBOT) has been used for the past 50 years to treat conditions such as decompression sickness, carbon monoxide poisoning and radiation damage. In recent years, studies have confirmed that HBOT has good effects in improving cognitive impairment after brain injury and stroke, and alleviating neurodegeneration and dysfunction related to NDD. Here we reviewed the pathogenesis and treatment state of NDD, introduced the application of HBOT in animal models and clinical studies of NDD, and expounded the application potential of HBOT in the treatment of NDD from the perspective of mitochondrial function, neuroinflammation, neurogenesis and angiogenesis, oxidative stress, apoptosis, microcirculation and epigenetics.
Hyperbaric Oxygenation ; Humans ; Neurodegenerative Diseases/physiopathology* ; Animals ; Oxidative Stress ; Apoptosis ; Mitochondria/physiology* ; Neurogenesis ; Epigenesis, Genetic

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

A combination of the "disease-syndrome-symptom" approach was used to study the syndrome characterization and features of dampness-heat obstruction syndrome in papain-induced knee osteoarthritis(KOA) model rats during the disease process. Forty-eight male SD rats were randomly divided into sham and model groups. The KOA model was established by injecting a mixture of papain and L-cysteine into the joint cavity on days 1, 3, and 5. During the 8 weeks following model establishment, the rats were assessed weekly for the plantar mechanical pain threshold, knee joint diameter, local skin temperature of the knee joint, weight-bearing difference between the two hind feet, and the modified Lequesne MG score of the knee joint. Samples were collected at 1, 2, 4, 6, and 8 weeks after model establishment to observe the gross lesions in cartilage and synovium. Histopathological changes in joint tissues were examined using hematoxylin-eosin, Masson's trichrome, and Senna red O-solid green staining. ELISA and immunohistochemical analysis were performed to detect the levels of interleukin(IL)-1β, IL-6, tumor necrosis factor(TNF)-α, prostaglandin E2(PGE2), and the expression of aquaporins(AQP) 1 and 3 in serum and synovium. The results showed that the ink score of articular cartilage in the model group significantly increased from 4 to 8 weeks, the cartilage Mankin's score and the percentage of Masson-positive area in cartilage increased significantly from 1 to 8 weeks. The percentage of red-stained area for cartilage proteoglycans decreased significantly from 1 to 8 weeks. The synovitis score from 1 to 6 weeks and the percentage of blue-stained collagen fibers in the synovium from 1 to 8 weeks increased significantly, with statistically significant differences compared to the sham group. The mechanical pain threshold in the model group significantly decreased from 1 to 8 weeks, the knee joint diameter significantly increased from 1 to 6 weeks, and the local skin temperature of the knee joint, the weight-bearing difference between the two hind feet, and the modified Lequesne MG score from 1 to 5 weeks significantly increased, all with statistically significant differences compared to the sham group. The levels of IL-1β, IL-6, TNF-α, and PGE2 in serum and synovium of the model group significantly increased from 1 to 6 weeks. Serum TNF-α and PGE2, and synovial IL-1β, also significantly increased at 8 weeks. The levels of cartilage AQP1 and AQP3 significantly increased from 1 to 4 weeks, while synovial AQP1 and AQP3 increased significantly from 1 to 6 weeks, with all differences statistically significant compared to the sham group. In conclusion, papain-induced KOA rats exhibited pathological changes, including articular cartilage degeneration and synovial inflammation, within 1 week of induction. The KOA rats showed characteristics of dampness-heat obstruction syndrome, such as joint pain, swelling, elevated skin temperature, and decreased function, as well as increased inflammatory factors and AQP1、AQP3 in serum and joint tissues within 5 to 6 weeks of disease onset. These results provide an experimental model for studying the syndromes of KOA with dampness-heat obstruction syndrome.
Animals ; Male ; Rats, Sprague-Dawley ; Rats ; Osteoarthritis, Knee/physiopathology* ; Disease Models, Animal ; Humans ; Interleukin-1beta/metabolism* ; Interleukin-6/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Knee Joint/pathology*

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

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 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:To investigate the effects and underlying mechanism of ionizing radiation on the adipogenic of mesenchymal stem cells(MSCs).Methods:Mouse MSCs were cultured in vitro and treated with 2 Gy and 6 Gy radiation with 60Co,and the radiation dose rate was 0.98 Gy/min.Bulk RNA-seq was performed on control and irradiated MSCs.The changes of adipogenic differentiation and oxidative stress pathways of MSC were revealed by bioinformatics analysis.Oil Red O staining was used to detect the adipogenic differentiation ability of MSCs in vitro,and real-time fluorescence quantitative PCR(qPCR)was used to detect the expression differences of key regulatory factors Cebpa,Lpl and Pparg after radiation treatment.At the same time,qPCR and Western blot were used to detect the effect of inhibition of Nrf2,a key factor of antioxidant stress pathway,on the expression of key regulatory factors of adipogenesis.Moreover,the species conservation of the irradiation response of human bone marrow MSCs and mouse MSC was determined by qPCR.Results:Bulk RNA-seq suggested that ionizing radiation promotes adipogenic differentiation of MSCs and up-regulation of oxidative stress-related genes and pathways.The results of Oil Red O staining and qPCR showed that ionizing radiation promoted the adipogenesis of MSCs,with high expression of Cebpa,Lpl and Pparg,as well as oxidative stress-related gene Nrf2.Nrf2 pathway inhibitors could further enhance the adipogenesis of MSCs in bone marrow after radiation.Notably,the similar regulation of oxidative pathways and enhanced adipogenesis post irradiation were observed in human bone marrow MSCs.In addition,irradiation exposure led to up-regulated mRNA expression of interleukin-6 and down-regulated mRNA expression of colony stimulating factor 2 in human bone marrow MSCs.Conclusion:Ionizing radiation promotes adipogenesis of MSCs in mice,and oxidative stress pathway participates in this effect,blocking Nrf2 further promotes the adipogenesis of MSCs.Additionally,irradiation activates oxidative pathways and promotes adipogenic differentiation of human bone marrow MSCs.