Objective To systematically analyze the literature characteristics of Journal of Organ Transplantation since its inception. Methods Using the China National Knowledge Infrastructure (CNKI) academic journal full-text database as the data source, all articles published in the Journal of Organ Transplantation from January 2010 to August 2025 were retrieved. After excluding non-academic papers, a total of 1 568 research papers were included. R language 4.3.0, Bibliometrix package 3.2.1, and Citespace software were used to analyze the number of publications, publishing institutions, authors, keywords and other aspects. Results The number of publications in Journal of Organ Transplantation increased from an average of 82 articles per year in the early years after its inception to 113 articles per year in recent years, a growth of 37.8%. The geographical distribution of publishing institutions covers 32 provinces, cities and autonomous regions nationwide, mainly concentrated in the South China, East China and North China regions, and has now basically covered the central and western regions in recent years. The author collaboration network includes 45 authors distributed across 7 major collaboration clusters, forming a stable multi-level national research system centered on key university-affiliated hospitals. The high-frequency keywords are dominated by "liver transplantation" (425 times) and "kidney transplantation" (396 times). The theme evolution shows a clear three-stage characteristic: initially focusing on clinical technology application, deepening to immune mechanism exploration in the middle stage, and recently (since 2022) focusing on cutting-edge research areas such as xenotransplantation. Conclusions Journal of Organ Transplantation has witnessed the rapid development of China's organ transplantation cause, fully reflecting the research status and trends in China's organ transplantation field, and has provided an important platform for the future development and international cooperation in China's organ transplantation field.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Objective: To investigate the prevalence and influencing factors of hepatitis C virus (HCV) infection among drug users in Baoshan District, Shanghai Municipality, so as to provide insights into strengthening HCV intervention among drug users. Methods: Drug users under community management in Baoshan District from 2017 to 2023 were recruited. Demographic information, drug use behaviors, sexual behaviors and receipt of intervention service were collected through questionnaire surveys. Blood samples were collected for HCV antibody testing, and the prevalence of anti-HCV antibody was analyzed. Factors affecting the prevalence of anti-HCV antibody among drug users were analyzed using a multivariable logistic regression model. Results: A total of 2 801 drug users were surveyed, including 2 233 males (79.72%) and 568 females (20.28%). The majority of drug users were aged 40 to <60 years (1 663 drug users, 59.37%). The prevalence of anti-HCV antibody was 28.35%, showing an overall upward trend from 2017 to 2023 (P<0.05). Multivariable logistic regression analysis showed that females (OR=1.468, 95%CI: 1.169-1.844), 40 years and over (40 to <50 years, OR=2.441, 95%CI: 1.838-3.242; 50 to <60 years, OR=2.377, 95%CI: 1.787-3.161; 60 to 97 years, OR=1.637, 95%CI: 1.163-2.304), using traditional drugs (OR=2.488, 95%CI: 1.967-3.147) or mixed drugs (OR=2.950, 95%CI: 1.974-4.409), having injected drugs (not share needles, OR=3.649, 95%CI: 2.849-4.673; share needles, OR=3.532, 95%CI: 1.851-6.738) and never using condoms during sexual contacts with spouses/cohabitants in the past year (OR=1.975, 95%CI: 1.354-2.879) were associated with a higher prevalence of anti-HCV antibody; the educational level of high school/technical secondary school (OR=0.483, 95%CI: 0.280-0.835) or college and above (OR=0.280, 95%CI: 0.129-0.608) was associated with a lower prevalence of anti-HCV antibody. Conclusions The prevalence of anti-HCV antibody among drug users in Baoshan District showed an upward trend from 2017 to 2023. Gender, age, educational level, type of drugs, history of drug injection and never using condoms during sexual contacts with spouses/cohabitants were influencing factors for prevalence of anti-HCV antibody among drug users.

ObjectiveTo explore the changes in volatile components， total polysaccharides， enzyme activity， and chromaticity value of Myristicae Semen Koji（MSK） during the fermentation process， and conduct correlation analysis. MethodsBased on gas chromatography-mass spectrometry（GC-MS）， the changes of volatile components in MSK at different fermentation times were identified. The phenol sulfuric acid method， dinitrosalicylic acid method（DNS）， and carboxymethyl cellulose sodium salt method（CMC-Na） were used to investigate the total polysaccharide content， amylase activity， and cellulase activity during the fermentation process. Visual analysis technology was used to explore the changes in chromaticity values， revealing the fermentation process of MSK and the dynamic changes of various measurement indicators， partial least squares-discriminant analysis（PLS-DA） was used to explore the differential compounds of MSK at different fermentation degrees， and Pearson correlation analysis was used to explore the correlation between volatile components of MSK and total polysaccharides， enzyme activity， and chromaticity values. ResultsA total of 60 volatile compounds were identified from MSK， the relative contents of components such as （+）-α-pinene， β-phellandrene， β-pinene， （+）-limonene， and p-cymene obviously increased， while the relative contents of components such as safrole， methyl isoeugenol， methyleugenol， myristicin， and elemicin significantly decreased. During the fermentation process， the total polysaccharide content showed an upward trend， while the activities of amylase and cellulase showed an initial increase followed by a decrease， and reached their maximum value at 40 h. the overall brightness（L^*） and total color difference（ΔE^*） gradually increased， while the changes in red-green value（a^*） and yellow-blue value（b^*） were not obvious. PLS-DA results showed that MSK could be clearly distinguished at different fermentation times， and 13 differential biomarkers were screened out. Pearson correlation analysis results showed that the contents of α-terpinene， β-phellandrene， methyleugenol， β-cubebene and myristic acid had an obvious correlation with chromaticity values. ConclusionAfter fermentation， the volatile components， total polysaccharides， amylase activity， and cellulase activity of MSK undergo significant changes， and there is a clear correlation between them and chromaticity values， which reveals the dynamic changes in the fermentation process and related indicators of MSK， laying a foundation for the quality control.

During the career life cycle, workers may face various health problems such as occupational injuries, occupational diseases, and work-related diseases. How to comprehensively protect the health of workers is a crucial scientific issue that needs to be solved urgently. Workers show the characteristics of co-exposure to multiple occupational risks or co-existence of multiple health conditions in their occupational activities. Occupational injuries are closely related to occupational diseases and work-related diseases. To carry out prevention and control of occupational injuries in the context of "big health", we should further strengthen the systematic approach and highlight the concept of "overall process" and "all-round". That is to establish an occupational injury surveillance system covering the whole process of surveillance-assessment-intervention and the evaluation of intervention effects, and to set up the joint prevention and control strategy of occupational injuries, occupational diseases, and work-related diseases. This will promote the implementation of efficient and intensive health management at government, society, employers, workers and other levels to achieve all-round protection of workers' health. When exploring the possible effects of job burnout, occupational stress, comorbidity, and other factors on occupational injuries, the introduction of machine learning methods provides a new approach to identifying and analyzing the influencing factors of occupational injuries and to exploring potential underlying mechanisms.

Background Workers in the cable manufacturing industry are exposed to high-speed machinery and equipment for a long time, coupled with heavy workload, which poses significant risks to their physical health. However, the issue of occupational injuries in this industry has not received enough attention yet. Objective To understand the incidence of occupational injury of workers in cable manufacturing industry and to analyze the influencing factors. Method A basic information questionnaire and an occupational injury questionnaire were developed to investigate the occupational injuries of 1 343 workers in a cable manufacturing enterprise in the past year, and a total of 1 225 valid questionnaires were recovered, with an effective rate of 91.2%. Descriptive statistics were used to characterize the causes, injury locations, injury types, and other characteristics of employees’ occupational injuries. Chi-square test was used to analyze the occupational injury status of groups with different demographic characteristics, occupational characteristics, lifestyles, and interpersonal relationships. Logistic regression was used to analyze the influencing factors of occupational injuries. Result The incidence of occupational injuries among workers in a cable manufacturing enterprise in the past year was 8.6%, which mainly happened in male workers (80.0%) and occurred from May to July in summer (45.7%). The main causes were mechanical injuries (32.4%) and object blows (27.6%). The main sources of damage were machinery and equipment (36.2%) as well as raw materials and products (15.2%). The main injuries were located in upper limbs (53.3%) and lower limbs (22.9%). The main types of injuries were fractures (33.3%) and abrasions/contusions/puncture wounds (19.0%). The results of univariate analysis showed that there were statistically significant variations in the incidence of occupational injuries by gender, overtime, pre-job training, years of service in current position, alcohol consumption, physical exercise per week, and co-worker relationship (P<0.05). The logistic regression model showed that workers who exercised less than twice a week, did not participate in pre-job training, worked overtime, and had fair/poor/very poor colleague relationship had a higher risk of occupational injury, while women had a lower risk of occupational injury. Conclusion The distribution of occupational injury population is mainly male, and the time distribution is mainly from May to July. Gender, physical exercise, pre-job training, overtime, and colleague relationship are the influencing factors of occupational injuries. We should strengthen pre-job training, arrange work hours reasonably, and create a good working atmosphere to reduce the occurrence of occupational injuries.

Background Social psychological factors have emerged as a key area of research in occupational injury prevention. Occupational stress, a significant component of social psychology, has garnered widespread attention due to its potential impact on occupational injury. Objective To analyze the factors influencing occupational stress among cable manufacturing workers and explore the relationship between occupational stress and occupational injury, and to provide scientific evidence for reducing occupational stress and injury. Methods A questionnaire on basic demographics, occupational injury, and occupational stress (Effort-Reward Imbalance, ERI) was used to investigate 1343 workers in a cable enterprise. Chi-square test was employed to analyze the distribution of occupational stress across different worker characteristics. Logistic regression was used to identify factors influencing occupational stress, and a decision tree model was applied to assess the impact of occupational stress on injury risk. Results A total of 1225 valid questionnaires were collected, yielding a response rate of 91.2%. The positive rate of occupational stress was 28.7%, while the incidence of occupational injury in the past year was 8.6%. The results of univariate analysis revealed that workers who had experienced occupational injuries had a higher positive rate of occupational stress compared to those without injuries (P<0.05), and there were significant differences in the distribution of occupational stress by genders, overtime work, shift work, daily working hours, fatigue levels, sleep duration, family relationships, and colleague relationships (P<0.05). The logistic regression results showed that individuals who worked shifts, had fair/poor/very colleague relationships, and frequently worked overtime had a higher risk of occupational stress (P<0.05). The final decision tree model confirmed that occupational stress was a risk factor for occupational injury. Additional factors influencing occupational injury included physical exercise, colleague relationships, pre-job training participation, and gender. Workers who exercised less than twice per week and experienced occupational stress had a significantly higher risk of injury (23.6%) compared to those without occupational stress (10.9%). Conclusion Shift work, colleague relationships, and overtime work are correlated with the positive rate of occupational stress among cable manufacturing workers. Implementing a reasonable work schedule and fostering a positive work environment are recommended. Reducing occupational stress may help lower the risk of occupational injuries in this industry.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

Objective: To systematically analyze the revisions content and technological development trends of microbiological standards in the Chinese Pharmacopoeia (ChP) 2025 Edition, and explore its novel requirements in risk-based pharmaceutical product lifecycle management.
Methods: A comprehensive review was conducted on 26 microbiological-related standards to summarize the revision directions and scientific implications from perspectives including the revision overview, international harmonization of microbiological standards, risk-based quality management system, and novel tools and methods with Chinese characteristics.
Results: The ChP 2025 edition demonstrates three prominent features in microbiological-related standards: enhanced international harmonization, introduced emerging molecular biological technologies, and established a risk-based microbiological quality control system.
Conclusion: The new edition of the Pharmacopoeia has systematically constructed a microbiological standard system, which significantly improves the scientificity, standardization and applicability of the standards, providing a crucial support for advancing the microbiological quality control in pharmaceutical industries of China.

ObjectiveUlcerative colitis is a prevalent immunoinflammatory disease. Th17/Treg cell imbalance and gut microbiota dysregulation are key factors in ulcerative colitis pathogenesis. The actin cytoskeleton contributes to regulating the proliferation, differentiation, and migration of Th17 and Treg cells. Wdr63, a gene containing the WD repeat domain, participates in the structure and functional modulation of actin cytoskeleton. Recent research indicates that WDR63 may serve as a regulator of cell migration and metastasis via actin polymerization inhibition. This article aims to explore the effect of Wdr63 deletion on Th17/Treg cells and ulcerative colitis. MethodsWe constructed Wdr63^-/- mice, induced colitis in mice using dextran sulfate sodium salt, collected colon tissue for histopathological staining, collected mesenteric lymph nodes for flow cytometry analysis, and collected healthy mouse feces for microbial diversity detection. ResultsCompared with wild-type colitis mice, Wdr63^-/- colitis mice had a more pronounced shortening of colonic tissue, higher scores on disease activity index and histological damage index, Treg cells decreased and Th17 cells increased in colonic tissue and mesenteric lymph nodes, a lower level of anti-inflammatory cytokine IL-10, and a higher level of pro-inflammatory cytokine IL-17A. In addition, WDR63 has shown positive effects on maintaining intestinal microbiota homeostasis. It maintains the balance of Bacteroidota and Firmicutes, promoting the formation of beneficial intestinal bacteria linked to immune inflammation. ConclusionWdr63 deletion aggravates ulcerative colitis in mice, WDR63 inhibits colonic inflammation likely by regulating Th17/Treg balance and maintains intestinal microbiota homeostasis.