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 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.

Objective: To investigate the prevalence of hypertension, diabetes and hyperlipidemia among HIV/AIDS patients aged 50 years and above receiving antiretroviral therapy (ART) in Wuxi City, Jiangsu Province, so as to provide insights into the prevention and intervention of chronic diseases for these populations. Methods: The HIV/AIDS patients aged 50 years and above receiving ART were recruited at designated HIV/AIDS medical institutions in Wuxi City using the convenient sampling method from March to June 2024. Demographic information, treatment status and self-reported prevalence of hypertension, diabetes and hyperlipidemia were collected through questionnaire surveys. Factors affecting the prevalence of chronic diseases were analyzed using a multivariable logistic regression model. Results: A total of 830 HIV/AIDS patients receiving ART were surveyed, including 656 males (79.04%) and 375 patients aged 50 to <60 years (45.18%). Among them, 366 patients reported having at least one type of chronic disease, including hypertension, diabetes and hyperlipidemia, with a self-reported prevalence rate of 44.10%. Specifically, 280, 114 and 61 patients reported having hypertension, diabetes and hyperlipidemia, with the self-reported prevalence rates of 33.73%, 13.73% and 7.35%, respectively. Multivariable logistic regression analysis showed that male patients (OR=1.725, 95%CI: 1.187-2.507), those with monthly income less than 3 000 yuan (OR=1.521, 95%CI: 1.122-2.063), those with body mass index of 24 kg/m2 and above (OR=1.577, 95%CI: 1.168-2.130), those who initiated ART at ages of 50 years and above (50 to <60 years, OR=1.535, 95%CI: 1.052-2.238; ≥60 years, OR=3.322, 95%CI: 2.191-5.038), those with ART duration of 10 years and above (OR=2.069, 95%CI: 1.419-3.017), and those who received non-first-line regimens (OR=1.776, 95%CI: 1.304-2.418) had higher risks of developing at least one type of chronic disease, including hypertension, diabetes and hyperlipidemia. Conclusions The self-reported prevalence of at least one type of chronic disease, including hypertension, diabetes and hyperlipidemia among HIV/AIDS patients aged 50 years and above receiving ART in Wuxi City was 44.10%. Gender, monthly income, body mass index and ART status are the main influencing factors for the risk of chronic diseases.

BACKGROUND: The main cause of restenosis after percutaneous transluminal angioplasty (PTA) is the excessive proliferation and migration of vascular smooth muscle cells (VSMCs). Lin28a has been reported to play critical regulatory roles in this process. However, whether CCAAT/enhancer-binding proteins β (C/EBPβ) binds to the Lin28a promoter and drives the progression of restenosis has not been clarified. Therefore, in the present study, we aim to clarify the role of C/EBPβ-Lin28a axis in restenosis. METHODS: Restenosis and atherosclerosis rat models of type 2 diabetes ( n   =  20, for each group) were established by subjecting to PTA. Subsequently, the difference in DNA methylation status and expression of C/EBPβ between the two groups were assessed. EdU, Transwell, and rescue assays were performed to assess the effect of C/EBPβ on the proliferation and migration of VSMCs. DNA methylation status was further assessed using Methyltarget sequencing. The interaction between Lin28a and ten-eleven translocation 1 (TET1) was analysed using co-immunoprecipitation (Co-IP) assay. Student's t -test and one-way analysis of variance were used for statistical analysis. RESULTS: C/EBPβ expression was upregulated and accompanied by hypomethylation of its promoter in restenosis when compared with atherosclerosis. In vitroC/EBPβ overexpression facilitated the proliferation and migration of VSMCs and was associated with increased Lin28a expression. Conversely, C/EBPβ knockdown resulted in the opposite effects. Chromatin immunoprecipitation assays further demonstrated that C/EBPβ could directly bind to Lin28a promoter. Increased C/EBPβ expression and enhanced proliferation and migration of VSMCs were observed after decitabine treatment. Further, mechanical stretch promoted C/EBPβ and Lin28a expression accompanied by C/EBPβ hypomethylation. Additionally, Lin28a overexpression reduced C/EBPβ methylation via recruiting TET1 and enhanced C/EBPβ-mediated proliferation and migration of VSMCs. The opposite was noted in Lin28a knockdown cells. CONCLUSION Our findings suggest that the C/EBPβ-Lin28a axis is a driver of restenosis progression, and presents a promising therapeutic target for restenosis.
Animals ; Cell Proliferation/genetics* ; Cell Movement/genetics* ; Muscle, Smooth, Vascular/metabolism* ; Rats ; DNA Methylation/physiology* ; CCAAT-Enhancer-Binding Protein-beta/genetics* ; Male ; Myocytes, Smooth Muscle/cytology* ; Rats, Sprague-Dawley ; RNA-Binding Proteins/genetics* ; Cells, Cultured ; Coronary Restenosis/metabolism*

Several types of arthritis share the common feature that the generation of inflammatory mediators leads to joint cartilage degradation. However, the shared mechanism is largely unknown. H2BK120ub1 was reportedly involved in various inflammatory diseases but its role in the shared mechanism in inflammatory joint conditions remains elusive. The present study demonstrated that levels of cartilage degradation, H2BK120ub1, and its regulator WW domain-containing adapter protein with coiled-coil (WAC) were increased in cartilage in human rheumatoid arthritis (RA) and osteoarthritis (OA) patients as well as in experimental RA and OA mice. By regulating H2BK120ub1 and H3K27me3, WAC regulated the secretion of inflammatory and cartilage-degrading factors. WAC influenced the level of H3K27me3 by regulating nuclear entry of the H3K27 demethylase KDM6B, and acted as a key factor of the crosstalk between H2BK120ub1 and H3K27me3. The cartilage-specific knockout of WAC demonstrated the ability to alleviate cartilage degradation in collagen-induced arthritis (CIA) and collagenase-induced osteoarthritis (CIOA) mice. Through molecular docking and dynamic simulation, doxercalciferol was found to inhibit WAC and the development of cartilage degradation in the CIA and CIOA models. Our study demonstrated that WAC is a key factor of cartilage degradation in arthritis, and targeting WAC by doxercalciferol could be a viable therapeutic strategy for treating cartilage destruction in several types of arthritis.

Objective:To study the infection and epidemiological characteristics of soil borne nematode disease in Sihong County, Suqian City, and to provide scientific basis for further development of prevention and control measures.Methods:In 2022, according to geographic location, Sihong County was divided into east and west areas. Jieji Town, Shiji Township, and Linhuai Town were selected from the east area, and Tianganghu Township, Meihua Town from the west area. One administrative village was selected as a survey point in each township. Using cluster sampling method, 450 permanent residents aged 3 years old and older in the village were selected as the respondents for questionnaire survey and fecal sample collection. The infection status of hookworms, roundworms, whipworms and pinworms in fecal samples were examined, transparent tape anal swab method for detecting pinworm eggs in children, and influencing factors were analyzed.Results:A total of 2 264 survey subjects were included, 18 cases of soil borne nematodes were detected as positive, with an overall positive rate of 0.80%. Among them, 15 cases were positive for hookworms, 1 case was positive for whipworms, and 2 cases were positive for pinworms, with detection rates of 0.66%, 0.04%, and 0.09%, respectively. No ascaris lumbricoides were detected. Transparent tape anal swab method was used to examine 142 children, and the positivity rate of 1.41% (2/142). By township, the detection rate of soil borne nematodes was the highest in Jieji Town, at 2.41% (11/457); the others were Meihua Town, Tianganghu Township, Shiji Township, and Linhuai Town, with detection rates of 0.89% (4/451),0.66% (3/454), 0 (0/451) and 0 (0/451), respectively. There was a statistically significant difference between different townships (χ 2 = 19.21, P < 0.001). Among the 18 positive cases of soil borne nematode, 7 were males and 11 were females, with detection rates of 0.66% (7/1 063) and 0.92% (11/1 201), respectively, the difference was not statistically significant (χ 2 = 0.47, P = 0.491). The age distribution showed the highest detection rate in the 40 - < 60 age group, at 1.42% (9/634), with statistically significant differences between different age groups (χ 2 = 6.41, P = 0.033). The occupational distribution showed the highest detection rate in farmers, at 1.46% (9/617), with statistically significant differences between different professions (χ 2 = 8.00, P = 0.034). The differences in total soil borne nematode and hookworm detection rates were statistically significant among different methods of treating human and animal feces (χ 2 = 11.01, 9.02, P = 0.003, 0.011). Conclusions:The main species of soil borne nematode infections in Sihong County, Suqian City are hookworms, with fewer infections of whipworms and pinworms, and no roundworm infections observed. There are regional differences in detection rate. In the future, it is necessary to strengthen health education for key populations, enhance health knowledge publicity, and effectively intervene to change unhealthy production and lifestyle, further reducing the infection of soil borne nematodes in Suqian City.

Objective:To investigate the application value of major anatomical structure recognition model of minimally invasive liver resection based on deep learning.Methods:The retrospective and descriptive study was conducted. The 31 surgical videos of laparoscopic left lateral sectionectomy performed in Zhujiang Hospital of Southern Medical University from January 2019 to April 2023 were collected. Video clips containing the surgical procedure of left lateral lobe liver pedicle and left hepatic vein were screened by 2 liver surgeons. After quality control, screening and frame extraction, the major anatomical structures on the images of these clips were annotated. After pre-processing, these images were transported to the DeepLab v3+neural network framework for model training. Observation indicators: (1) video annotation and classification; (2) results of arti-ficial intelligence anatomical recognition model testing. Measurement data with normal distribution were represented as Mean± SD, and count data were described as absolute numbers. Results:(1) Video annotation and classification. A total of 4 130 frames of images were annotated in the 31 surgical videos, including 2 083 frames of annotated images for the left lateral lobe liver pedicle, 1 578 frames of annotated images for the left hepatic vein and 469 frames of annotated images for both the left lateral lobe liver pedicle and left hepatic vein. (2) Results of artificial intelligence anatomical recognition model testing. In four application scenarios (clean scene, bloodstain scene, partially obstruction by instrument scene, and small exposed area scene), the model was able to successfully recognize the left lateral lobe liver pedicle and left hepatic vein, with a recognition speed for anatomical markers >13 frames/s. When performing anatomical recognition on images with only the left lateral lobe liver pedicle, the Dice coefficient, intersection over union, accuracy, sensitivity and specificity of the model were 0.710±0.110, 0.560±0.120, 0.980±0.010, 0.640±0.030, and 0.980±0.010, respectively. The above indicators of the model were 0.670±0.180, 0.530±0.200, 0.980±0.010, 0.600±0.040, and 0.990±0.010 when performing anatomical recognition on images with only the left hepatic vein, and 0.580±0.180, 0.430±0.190, 0.980±0.010, 0.580±0.020, and 0.990±0.010 when per-forming anatomical recognition on images with both the left lateral lobe liver pedicle and left hepatic vein.Conclusion:The major anatomical structure recognition model of minimally invasive liver resection based on deep learning can be applied in identifying liver pedicle and hepatic vein.