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 As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. Objective To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. Methods A total of 2243 frontline workers from cable and shipbuilding enterprises were selected as study subjects to investigate the incidence of non-fatal occupational injuries and collect information at four levels: individual, equipment, management, and environment in past 12 months. Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. The influence degree and type of variables were judged based on the magnitude of the estimated coefficients of each variable, where variables with estimated coefficients > 0 are risk factors, and those <0 are protective factors. The area under the receiver operating characteristic (ROC)curve (AUC) was used to test the performance of the model, with an AUC value > 0.7 indicating good model performance. Results Among the 2243 frontline workers, males accounted for 77.7% (1742 out of 2243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2243), 82.7% of the workers (1854 out of 2243) were married, and 55.6% (1248 out of 2243) had a junior middle school education level. The average monthly income for 51.0% (1144 out of 2243) of the workers was between 5000 and 6999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2243) in the past 12 months. Among the 22 factors associated with the occurrence of non-fatal occupational injuries (P<0.05), 10 were individual-level factors, including gender, smoking, alcohol consumption, colleague relationships, average exercise duration, job burnout, work fatigue, musculoskeletal disorders, cardiovascular diseases, and neurological and sensory organ diseases; 3 were equipment-level factors, including equipment operability, hazardous workpieces, and safety hazards; 5 were environmental-level factors, including low temperatures, special operations, noise, workspace size, and dirty and disorderly environment; and 4 were management-level factors, including daily working hours, weekly working days, overtime, and pre-job technical training. The AUC value of the LASSO regression model was 0.704 and the final model retained a total of 10 variables. Among them, there were 7 risk factors for non-fatal occupational injuries (coefficient > 0), including safety hazards, musculoskeletal disorders, dangerous workpieces, job burnout, dirty and disorderly environment, smoking, and male gender; and 3 protective factors (coefficient < 0), including pre-job technical training, good colleague relationship, and long working days per week. Conclusion Manufacturing enterprises need to focus on the incidence of non-fatal occupational injuries and conduct targeted interventions for non-fatal occupational injuries by controlling potential safety hazards, providing pre-job technical training, reducing dangerous workpieces, rectifying working environment, and reasonably arranging working hours.

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 Construction workers represent a high risk group for occupational injuries. Currently, domestic and international studies examining the factors affecting occupational injuries among construction workers focus on demographic and behavioural characteristics. However, there is limited attention to psychosocial, use of digital technology, and health status of workers. Objective To analyze the occurrence of occupational injuries among workers in the construction industry, explore impacts of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace on occupational injuries, and provide a basis for the development of preventive measures. Methods Publicly available data from the European Union Occupational Safety and Health Administration were retrieved, comprising a sample of 2167 construction workers. The outcome indicator was the presence of occupational injuries among workers. A total of 25 variables in the dimensions of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace were extracted after Chi-square test, and then a combination of Boruta's algorithm and logistic regression was applied to identify the key factors affecting occupational injuries. Results Among the 2167 construction workers surveyed, 182 (8.6%) reported experiencing occupational injuries. The Boruta algorithm identified eight characteristics which in descending order of importance were musculoskeletal disorders, job type, depression and anxiety, level of completed education, use of electronic smart products, timely solution of safety problems, overall fatigue, and age. The logistic regression results indicated that six variables had statistically significant effects on occupational injuries: age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems (P<0.05). Conclusion Occupational injuries in construction workers are influenced by a variety of factors, including age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems. Companies and workers should take targeted measures to reduce the incidence of occupational injuries.

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.

Background Diseases severely affect the efficiency of workers. Comorbidity refers to the coexistence of two or more chronic diseases or health problems in the same individual. Previous studies have primarily focused on occupational injuries caused by environmental exposures, while the analysis of the epidemiological characteristics of self-reported diseases and occupational injuries among manufacturing workers has been insufficient. Objective To analyze the distribution of self-reported diseases and occupational injuries among manufacturing workers, the strength of correlation between different diseases, and common disease combinations, and to preliminarily explore the relationship between self-reported diseases and occupational injuries. Methods A cross-sectional survey was conducted to investigate the occupational injuries of 2382 workers in 2 manufacturing enterprises over the past year, and 2355 questionnaires were valid after cleaning. The questionnaire included three parts: basic information, occupational injury occurrence, and disease prevalence. The self-reported diseases diagnosed by physicians included musculoskeletal diseases, cardiovascular diseases, respiratory diseases, mental health problems, neurological and sensory organ diseases, digestive organ diseases, reproductive and urinary organ diseases, skin diseases, tumors, endocrine and metabolic system diseases, blood diseases and birth defects, a total of 11 types. Log-binomial model was used to calculate the prevalence ratio (PR) value of the association between different diseases, and UpSet diagram was used to present the disease combination pattern. Results In total, 40.2% (946/2355) of the study participants reported at least one disease, while 8.6% (203/2355), 5.2% (122/2355), and 5.9% (140/2355) reported having two, three, and four or more diseases, respectively. A higher prevalence was observed for musculoskeletal diseases, neurological and sensory organ diseases, digestive organ diseases, and skin diseases, which accounted for 18.0% (423/2355), 13.8% (324/2355), 9.3% (218/2355), and 9.1% (214/2355), respectively. Workers with musculoskeletal disorders, cardiovascular diseases, or neurological and sensory organ diseases reported significantly higher incidence of occupational injuries compared with workers without the corresponding diseases (P<0.001, P=0.041, and P=0.006, respectively). Musculoskeletal diseases were strongly associated with comorbidities of neurological and sensory organ diseases (PR values were 1.86 and 1.98, respectively), and the other patters were musculoskeletal diseases and digestive organ diseases (PR and OR values were 1.57 and 2.35, respectively), and musculoskeletal diseases and cardiovascular diseases (PR and OR values were 1.46 and 2.22, respectively). The largest binomial comorbidity group involved musculoskeletal diseases and neurological and sensory organ diseases, accounting for 5.4% (25/465) of the comorbid workers. Conclusion Among the self-reported diseases of manufacturing workers, musculoskeletal diseases, neurological and sensory organ diseases, digestive organ diseases, and skin diseases are more common. Musculoskeletal diseases and neurological and sensory organ diseases are the most common in all comorbidities. Musculoskeletal diseases, cardiovascular diseases, and neurological and sensory organ diseases are associated with occupational injuries. In the prevention and control of occupational injuries, joint prevention strategies targeting multiple diseases should be strengthened.

BACKGROUND:Small intestinal submucosal acellular matrix has been proven by clinical and basic studies to be useful for urethral repair and reconstruction.However,when applied alone,it has problems such as slow growth of host cells,difficulty in survival due to insufficient stent vascularization,and obstruction of reconstructed urethral stricture,and is only suitable for short urethral stricture.OBJECTIVE:To investigate the feasibility of constructing tissue engineered urethra with acellular matrix combined with exosomes in small intestinal submucosa.METHODS:Exosomes were isolated from rabbit bone marrow mesenchymal stem cells.The porcine small intestinal submucosal acellular matrix was prepared,and exosomes were loaded on the porcine small intestinal submucosal acellular matrix.The extracellular stroma-exosome(PKH26 dye labeled)complex of small intestinal submucosa was co-cultured with umbilical vein endothelial cells for 12 hours to observe the uptake of exosomes.The umbilical vein endothelial cells with good growth status were selected and cultured in three groups:the blank group was cultured routinely.The control group was added with small intestinal submucosal acellular matrix,and the experimental group was added with small intestinal submucosal acellular stromat-exosome complex.The angiogenesis was evaluated by scratch test,tube formation test,and angiogenic factor secretion test.Thirty New Zealand white rabbits were selected to establish a long(3 cm)urethral defect model,and the intervention was divided into three groups by random number table method(n=10).The single material group was implanted with small intestinal submucosal acellular matrix.The control group was implanted with small intestinal submucosal acellular matrix-bone marrow mesenchymal stem cell complex.The experimental group was implanted with small intestinal submucosal acellular matrix-exosome complex.Urethrography,urodynamic examination,and pathological observation of reconstructed urethral sections were performed 12 weeks after implantation.RESULTS AND CONCLUSION:(1)Exosomes in the acellular matrix of small intestinal submucosa could be taken up by umbilical vein endothelial cells under the fluorescence microscope.(2)Compared with the blank group and the control group,the experimental group could promote the migration of umbilical vein endothelial cells,angiogenesis ability,and the secretion of angiogenic factors vascular endothelial growth factor,hepatocyte growth factor,and interleukin 8(P<0.05).(3)Urethrography results showed that all 10 rabbits in the single material group had urethral stenosis;2 out of 10 rabbits in the control group had urethral stenosis,and none of the 10 rabbits in the experimental group had urethral stenosis.The results of urodynamic examination showed that the maximum urethral pressure at 12 weeks after implantation was higher in the single material group than before surgery(P<0.05),and the maximum urethral pressure at 12 weeks after implantation was lower in the control group and the experimental group than in the blank group(P<0.05).Hematoxylin-eosin,Masson and immunohistochemical staining showed that in the single material group,there were obvious regenerated epidermis,a small amount of subcutaneous smooth muscle and blood vessels,mainly fibrous tissue hyperplasia,accompanied by obvious inflammatory cell infiltration.In the control group,there were more complete regenerated epithelium and a small amount of collagen,a large number of subcutaneous blood vessels and smooth muscle,accompanied by inflammatory cell infiltration.The experimental group showed complete regenerated epidermis,a large number of subcutaneous blood vessels and smooth muscle,and no obvious inflammatory cell infiltration.The positive expressions of AE1/AE3,alpha smooth muscle actin,and CD31 in the experimental group were higher than those in the single material group and the control group(P<0.05).(4)The results show that the small intestinal submucosal acellular matrix-exosome tissue engineered urethra can repair the urethral defect by promoting angiogenesis.

Objective:To investigate the clinical features, treatment strategies, and prognosis of pediatric Castleman disease (CD), with the aim of enhancing disease recognition and improving diagnostic and therapeutic approaches.Methods:A retrospective analysis was conducted on the clinical data of 29 children diagnosed with CD at the Children′s Hospital, Zhejiang University School of Medicine, between January 2016 and January 2024. Age, sex, initial presentations, routine blood test, inflammatory indicators, coagulation function test, immunoglobulin level, histopathological examination, imaging examination, treatment and follow-ups were recorded. Patients were classified into two groups based on the presence or absence of symptoms: the symptomatic Castleman disease group (SC) and the asymptomatic Castleman disease group (AC). All patients were followed for a minimum of six months. Clinical characteristics, therapeutic regimens, and outcomes were compared between the two groups.For the measurement information, the independent samples t test was used for comparison between groups of data that conformed to normal distribution; the Mann-Whitney U test was used for comparison between groups of data that did not conform to normal distribution. The Chi-square test or Fisher′s exact probability method was used for comparison between groups of count data. Results:A total of 29 pediatric CD cases were included, comprising 15 males and 14 females. The number of patients diagnosed as unicentric Castleman disease (UCD) was 21, with the rest 8 as multicentric Castleman disease (MCD). There were 11 patients (37.9%) in the SC group and 18 patients (62.1%) in the AC group. The median age at onset was 10.0 years (interquartile range: 6.5-12.2 years). The most common initial presentation was mass ( n=20), followed by fever ( n=2), amaemia ( n=2), fever with fatigue ( n=2), rash ( n=1), abdominal pain ( n=1), and cough ( n=1). The frequently involved sites were neck ( n=8), abdominal cavity ( n=5), mediastinum ( n=3), retroperitoneum ( n=2), and one case each in the back, upper arm, and pelvis. The maximum lesion volume reached 1 040 cm 3. Compared to the AC group, the SC group had significantly lower hemoglobin levels [108.0(92.0, 123.0)g/L vs. 127.0(117.5, 139.0)g/L, Z=-2.35, P=0.019] and significantly higher levels of C-reactive protein (CRP) [38.0(3.0, 87.0)mg/L vs. 0.6(0.5, 3.8)mg/L, Z=-2.19, P=0.029], prothrombin time[12.6(11.4, 13.3)s vs. 11.3(10.5, 11.7)s, Z=-2.64, P=0.008], and fibrinogen [4.5(3.5, 5.4)g/L vs. 2.1(1.9, 2.6)g/L, Z=-3.04, P=0.002]. All patients underwent MRI/CT and ultrasonography; however, only 2 cases were diagnosed as CD by ultrasound. All patients underwent surgical excision of the mass, and diagnoses were confirmed by histopathological examination. Among the SC group, 4 patients (4/11, 36.4%) required postoperative treatment with glucocorticoids alone or in combination with immunosuppressants and biologics. The overall prognosis was favorable, with follow-up ranging from 6 months to 7 years and no reported mortality. One case, initially misdiagnosed as systemic lupus erythematosus, was later confirmed to be CD complicated with paraneoplastic pemphigus (PNP); this patient experienced recurrent fever, rash, and elevated CRP, and continued to require low-dose glucocorticoids and tacrolimus during the 4-year follow-up. Conclusion:In chileren, CD is most common in the neck and surgical resection yields favorable outcomes. Approximately 37.9% of patients present with systemic symptoms, of which about 36.4% require postoperative systemic therapy such as glucocorticosteroids. CD complicated with PNP may be misdiagnosed. Symptomatic cases are often associated with anemia, elevated CRP levels, and coagulation abnormalities. UCD pediatric patients without complications have a good prognosis and there′s no need for long-term medications after surgery. About 37.5% of MCD pediatric patients require glucocorticoids, immunosuppressants, biotherapy or chemotherapy to control symptoms in the postoperative period. Imaging modalities have limited diagnostic value, and histopathological examination remains the gold standard for diagnosis.

Objective:To explore the application and clinical significance of pathological diagnostic criteria for medically refractory epilepsy in children.Methods:Cross-sectional study.A retrospective analysis was conducted on 490 children(pathology involved) with medically refractory epilepsy treated continuously in the Pediatric Epilepsy Center of Peking University First Hospital from January 2019 to May 2022.The distribution of different pathological types was observed, and the differences in clinical characteristics among different pathological types were analyzed through Kruskal-Wallis or χ2 tests.The impact of clinical and pathological features on patient prognosis was evaluated through regression analysis. Results:Focal cortical dysplasia (FCD) was the predominant lesion (49.59%, 243/490).The electroencephalograms ( χ2=6.720, P=0.035) and clinical seizure characteristics ( χ2=26.370, P<0.001) in FCDⅡ were more focal than those in FCD Ⅰ and Ⅲ.Moreover, the proportions of focal resection in surgery ( χ2=24.286, P<0.001) and central involvement ( χ2=22.849, P<0.001) in FCDⅡ were higher than those in FCD Ⅰ and Ⅲ.Univariate and multivariate regression analyses revealed that FCD Ⅱ had a better prognosis than other dysplastic patients among the 375 cases of dysplasia ( P=0.049).Next-generation sequencing was performed on 35 cases of cortical malformations with such morphological characteristics as increased numbers of neurons in the white matter and Olig2-positive glial cell hyperplasia, and SLC35A2 mutations were detected in 2 cases (5.71%). Conclusions:Pathology of refractory epilepsy is specialized and continuously evolving.Standardized specimen processing and the accumulation of morphological, immunohistochemical, and molecular genetic data provide the foundation for clarifying the neuropathological nature of epilepsy, improving integrated classification, and advancing prognosis prediction and targeted therapy.