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 Prior epidemiological studies suggest that exposure to pyrethroid insecticides may adversely affect children’s respiratory health. However, only limited studies are currently available on this topic in China. Objective To explore the association between exposure to pyrethroid insecticides and pulmonary function in children in Shanghai. Methods From August 2019 to January 2020, a cross-sectional study was conducted, recruiting 163 healthy school-aged children (aged 5–12 years) from Shanghai Children’s Hospital, Shanghai Jiao Tong University School of Medicine. Basic information, including age, height, weight, and family income, was collected. Urine samples from the children were collected and were analyzed for the levels of three pyrethroid insecticide metabolites: 3-phenoxybenzoic acid (3-PBA), cis-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (CDCCA), and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (TDCCA). Gas chromatography-tandem mass spectrometry (GC-MS/MS) was used for the analysis. Spirometry was used to assess pulmonary function and recorded following parameters: peak expiratory flow (PEF), forced expiratory flow between the 25th and 75th percentiles of forced vital capacity (FEF_25-75), forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC. Multiple linear regression and restricted cubic spline models were used to evaluate the associations between urinary pyrethroid insecticide metabolite levels and pulmonary function parameters. Results The study included 163 school-aged children, with an average age of (7.04 ± 2.08) years and an average body mass index (BMI) of (16.04 ± 2.72) kg·m⁻²; 75 (46.01%) of the participants were boys. The detection rates of 3-PBA, TDCCA, and CDCCA in urine were 85.28%, 17.79%, and 4.91%, respectively. The median creatinine-adjusted 3-PBA concentration was 0.150 μg·g⁻¹. After adjusting for confounders such as height, BMI, sex, age, delivery mode, annual family income, and maternal education level, the multiple linear regression model showed that urinary 3-PBA levels were negatively associated with both FVC [β=−0.030, 95% confidence interval (CI): −0.058, −0.003; P=0.031] and FEV₁ (β=−0.032, 95%CI: −0.064, 0.000; P=0.04998). The final restricted cubic spline model showed a nonlinear association between urinary 3-PBA levels and FVC, FEV₁, PEF, and FEF_25-75（P for nonlinear < 0.05；P for overall < 0.05）. Conclusion The level of pyrethroid insecticide exposure in school-aged children in Shanghai is relatively high. The urinary 3-PBA concentration is negatively associated with pulmonary function, indicating potential adverse effects of pyrethroid insecticide exposure on respiratory health of school-aged children.

Recently, there has been an increasing risk of importation of tropical diseases into China and the resultant re-transmission in the country with the in-depth implementation of the “Belt and Road” Initiative, which poses a serious threat to the national public health security. To effectively respond to the cross-border transmission risk of tropical diseases and facilitate the process towards tropical disease control and elimination in China and the countries along the “Belt and Road” Initiative, this article analyzes the current status and governance risks of major imported tropical diseases, cross-border joint prevention and control polices implemented for tropical diseases and challenges in the establishment of the joint prevention and control system for tropical diseases in China, and discusses the establishment and implementation path of the joint prevention and control system for tropical diseases in countries along the “Belt and Road” Initiative. This path covers the establishment of cross-border cooperation mechanisms, research and development and pilot production of Chinese public health products, and implementation of key cross-border tropical disease prevention and control projects. The establishment of this system will further improve Chinese prevention and control capabilities for key cross-border tropical diseases, build a demonstrative prevention and control model for tropical diseases, and promote international technical exchanges and cooperation of tropical diseases.

AIM: To investigate the changes in serum levels of trefoil factor 1(Tff1)and bone morphogenetic protein 4(BMP4)in patients with diabetic retinopathy, and to evaluate their diagnostic value for diabetic retinopathy.METHODS: From January 2022 to January 2024, 186 patients with type 2 diabetes were selected as the study group and divided into a retinopathy subgroup(52 cases)and a non-retinopathy subgroup(134 cases)based on the presence of retinopathy. Another 186 healthy volunteers who underwent physical examinations during the same period were chosen as the control group. Serum Tff1 and BMP4 levels were measured using the enzyme-linked immunosorbent assay(ELISA). Pearson analysis was used to assess the correlation between serum Tff1, BMP4 levels, and clinical data. Multivariate Logistic regression analysis was performed to identify factors influencing the development of retinopathy in type 2 diabetic patients. Receiver operating characteristic(ROC)curve analysis was conducted to evaluate the diagnostic value of serum Tff1 and BMP4 levels for retinopathy in type 2 diabetic patients.RESULTS: Compared to the control group, serum Tff1 levels were lower and BMP4 levels were higher in both retinopathy and non-retinopathy subgroups(all P<0.05). Specifically, serum Tff1 levels were lower and BMP4 levels were higher in the retinopathy subgroup than in the non-retinopathy subgroup(all P<0.05). Pearson analysis revealed that Tff1 levels in type 2 diabetes patients were negatively correlated with disease duration, glycated hemoglobin levels, and triglyceride levels, while BMP4 levels were positively correlated(all P<0.05). Multivariate Logistic regression analysis identified type 2 diabetes duration, glycated hemoglobin, triglycerides, Tff1, and BMP4 as influencing factors for retinopathy development in type 2 diabetes patients(all P<0.05). ROC curve analysis showed that the combined diagnosis of serum Tff1 and BMP4 had an area under the curve(AUC)of 0.901, which was significantly higher than that of Tff1 alone(Z=2.069, P=0.039)and BMP4 alone(Z=2.072, P=0.038).CONCLUSION: Serum Tff1 levels are decreased and BMP4 levels are increased in patients with diabetic retinopathy, and the combined detection of these two markers offers high diagnostic value for diabetic retinopathy.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

Microplastics have been identified as one of the four major emerging global pollutants, with its associated health risks becoming a forefront scientific issue in the fields of public health and ecological safety. Large amounts of microplastics have been detected in various environmental media, food products, and daily-use commodities which are in frequent contact with humans. Three primary pathways of human exposure to microplastics are inhalation, ingestion, and dermal contact. Current available studies have revealed that microplastics with diverse morphologies and dimensions have been identified in many human biological samples, including blood, liver, and placenta by spectroscopic and mass spectrometric techniques. However, the potential implications of microplastics on human health remain largely undefined. This review summarized and compared the exposure levels, size distributions, morphological characteristics, and chemical compositions of microplastics in different human tissues and organs. It also summarized current common analytical techniques for detecting microplastics in human biological samples, and analyzed the potential adverse effects of microplastic deposition on human tissues and organs. Finally, the limitations of current research were discussed, and a prospective outlook was provided on how to scientifically and effectively conduct investigation of microplastics in human populations.

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners