Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience of radiologists, which affects rapid detection on large populations. Recent research focuses on computer-aided detection based on machine learning. These have achieved high accuracy, among which artificial neural network (ANN) shows excellent performance. However, due to imbalanced samples and lack of interpretability, wide utilization in clinical practice meets difficulty. To address these problems, we first establish a pneumoconiosis radiograph dataset, including both positive and negative samples. Second, deep convolutional diagnosis approaches are compared in pneumoconiosis detection, and a balanced training is adopted to promote recall. Comprehensive experiments conducted on this dataset demonstrate high accuracy (88.6%). Third, we explain diagnosis results by visualizing suspected opacities on pneumoconiosis radiographs, which could provide solid diagnostic reference for surgeons.

Objective: This study aims to determine the occupational health status of workers exposed to dust and the risk factors of lung function decline, to provide a basis for formulating corresponding occupational disease-prevention strategies. Methods: Data on 2045 workers exposed to dust, including their age, gender, exposure time, chest X-ray test results, and pulmonary function test results, were obtained from a key occupational disease monitoring project in Chongqing, China, in 2021. Chi-square tests and multifactorial logistic regression, and other methods, were used for statistical analysis. Results: The prevalence of pneumoconiosis-like changes was 0.83% (17/2045), and the prevalence of abnormal forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC was 4.25% (87/2045), 12.81% (262/2045), and 1.47% (30/2045), respectively. With increasing worker’s age, the prevalence of abnormal pneumoconiosis-like changes (p = 0.0065), FEV₁ (p = 0.0002), FVC (p < 0.0001), and FEV₁/FVC (p = 0.0055) all increased. Factors such as age, exposure duration, enterprise size, and dust exposure concentration were associated with abnormal lung function. Conclusions: Workers exposed to occupational dust have a high rate of abnormal lung function. The government, enterprises, and individuals should pay attention to occupational dust exposure, and various effective measures should be actively taken to protect the life and health of workers.

Physical activity is needed in today’s highly sedentary society. Research into Chinese trends with respect to the longitudinal association between changes in physical activity (PA) and dyslipidemia (DL) is limited. The present study assessed PA and PA-related changes associated with the level of serum lipids, and the risk of dyslipidemia stratified by gender, via a population-based longitudinal observational study among adults (>18 years old) residing in nine provinces in China. Data of 3380 adult participants, gleaned from two surveys in 2009 and 2015 used by the China Health and Nutrition Survey (CHNS), were analyzed. Three-level random intercept growth models were used to calculate the associations between total physical activity (TPA), the four activity subtypes, and changes in the level of blood lipids. Three-level logistic regression models were utilized to assess the association between TPA and the risk of DL. The highest level of TPA was observed to correlate with the lowest prevalence of DL. Compared with the lowest level of TPA, the highest level of TPA increases the concentration of HDL-C (β = 0.050, 95% CI = 0.005, 0.096) in males, and also reduces the risk in males of having low HDL-C by 31% (OR = 0.69, 95% CI = 0.52, 0.91). Occupational PA was positively associated with higher HDL-C levels in both genders (males: β = 0.004, 95% CI = 0.002, 0.007; females: β = 0.002, 95% CI = 0.000, 0.004), and leisure-time physical activity (LTPA) was negatively associated with TG levels in males. Increasing TPA benefited HDL-C levels in males, irrespective of the level of basic TPA of individuals. Physical activity (especially occupational PA) was superior in positively influencing HDL-C in males, but not in females. Leisure-time physical activity needs to be increased substantially in order to increase TPA to levels that would be beneficial to blood lipid levels and DL, irrespective of baseline TPA levels.

Physical activity (PA) is of benefit and particularly important for cardiovascular disease risk factors as being sedentary becomes a lifestyle habit. Research into Chinese complex association among physical activity, body-fat percentage (BF%), blood pressure, and serum lipids is limited. The present study is based on an observational study among adults (>18 years old) residing in fifteen provinces in China. Data of 10,148 adult participants in the 2015 China Health and Nutrition Survey (CHNS) were analyzed. The simple mediation effect models with covariates were utilized to assess the association among PA and blood pressure or serum lipids, and BF% was played as a mediator. The serial multiple-mediator models with covariates were constructed to the further analysis of the relationship between PA and blood pressure, and BF% was the mediator 1 and blood lipids were the mediator 2. Based on the above hypothesis, the moderated mediation models with covariates were used to analyze the association among PA, BF%, and blood pressure; in addition, BF% was used as the mediator and blood lipids played as the moderator. In the simple mediation models, the model with a dependent variable was high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein cholesterol (LDL-C); BF% was played as the partly mediation effect and the proportion of contribution was 0.23 and 0.25, respectively. In the serial multiple-mediator models, blood lipids, as the second mediator, played the mediation effect; however, the effect was smaller than the BF%. In the moderated mediation model, blood lipids had the moderation effect as the moderator variable. HDL-C played a moderating role in the latter pathway of the “PA→BF%→SBP/DBP” mediation model, and LDL-C/TC played a moderating role in the direct effect of the “PA→BF%→DBP”. In conclusion, BF% played a mediating role in the relationship between PA and blood pressure. HDL-C, LDL-C, and TC were more likely to act as moderating variables in the mediation model “PA→BF%→SBP/DBP”. PA could directly and indirectly benefit to control the CVD risk factors simultaneously.

To identify the influencing factors and develop a predictive model for the risk of abnormal liver function in the automotive manufacturing industry works in Chongqing. Automotive manufacturing workers in Chongqing city surveyed during 2019–2021 were used as the study subjects. Logistic regression analysis was used to identify the influencing factors of abnormal liver function. A restricted cubic spline model was used to further explore the influence of the length of service. Finally, a deep neural network-based model for predicting the risk of abnormal liver function among workers was developed. Of all 6087 study subjects, a total of 1018 (16.7%) cases were detected with abnormal liver function. Increased BMI, length of service, DBP, SBP, and being male were independent risk factors for abnormal liver function. The risk of abnormal liver function rises sharply with increasing length of service below 10 years. AUC values of the model were 0.764 (95% CI: 0.746–0.783) and 0.756 (95% CI: 0.727–0.786) in the training and test sets, respectively. The other four evaluation indices of the DNN model also achieved good values.