Ling Xue

Objective: To understand the spatiotemporal distribution characteristics, epidemiological features, and long-term trends in the incidence of pulmonary tuberculosis in Jining City from 2010 to 2024, and to provide scientific evidence for the formulation of targeted prevention and control strategies. Methods: Spatial autocorrelation and spatiotemporal scan analyses were used to study the spatial distribution characteristics and spatiotemporal clustering of tuberculosis incidence in Jining City. The Joinpoint regression and age–period–cohort models were employed to analyze the long-term trends of tuberculosis incidence, as well as the impact of age, period, and birth cohort. Results: From 2010 to 2024, 43,816 tuberculosis cases were reported in Jining City, with an overall average annual incidence rate of 35.31 per 100,000. Global spatial autocorrelation showed that cases were randomly distributed in most years, with significant clustering only in 2010, 2016, and 2019. Local autocorrelation analysis indicated that the central and southwestern areas of Jining City exhibited low-low clustering, while the northern and northeastern areas showed high-low and high-high clustering. Spatiotemporal scan analysis further detected various spatiotemporal clusters in certain regions, including Sishui County, Qufu City, Wen Shang County, and Jiaxiang County. The Joinpoint regression model indicated a significant downward trend in incidence across all groups, with the fastest average decrease observed among males. The age–period–cohort model showed that age, period, and cohort all significantly influenced the incidence of tuberculosis. Net drift and local drift values confirmed a declining trend, with the slowest decrease in the 20–24 age group. The age effect demonstrated an initial rise followed by a decline in incidence rate, peaking in the 20–24-year age group; the period effect showed a declining trend in tuberculosis risk over time in Jining City; the cohort effect indicated that the later the birth cohort, the lower the risk of developing tuberculosis. Conclusion: Although the incidence of tuberculosis in Jining City is on a declining trend, localized hotspots and certain spatiotemporal clusters have been identified in specific counties and periods. Furthermore, the disease incidence is influenced by factors such as age, period, and cohort. Further targeted measures should be taken to curb disease spread.

Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.

The Chinese Visceral Adiposity Index (CVAI) is an indicator of visceral adiposity dysfunction used to evaluate the metabolic health of the Chinese population. Steelworkers are more likely to be obese due to their exposure to special occupational factors, and have a higher prevalence of carotid atherosclerosis (CAS). This study aimed to analyze the special relationship between CVAI and CAS among steelworkers. A total of 4075 subjects from a northern steel company were involved in the cross-sectional study. Four logistic regression models were developed to analyze the correlation between CVAI and CAS. In addition, the restricted cubic spline was applied to fit the dose–response association between CVAI and CAS risk. In the study, the prevalence of CAS was approximately 25.94%. After adjustment for potential confounders, we observed a positive correlation between CVAI and CAS risk. Compared to the first CVAI quartile, the effect value odds ratio (OR) and 95% CI in the second, third, and fourth CVAI quartile were 1.523 (1.159–2.000), 2.708 (2.076–3.533), and 4.101 (3.131–5.372), respectively. Additionally, this positive correlation was stable in all subgroups except for female. Furthermore, we also found a non-linear relationship between CVAI and CAS risk (p nonlinear < 0.05). Notably, CVAI could increase the risk of CAS when higher than 106. In conclusion, our study showed that CVAI might be a reliable indicator to identify high-risk populations of CAS among steelworkers.

OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability. RESULTS: The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models. CONCLUSION: The prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.

Objective: To process and extract electrocardiogram (ECG, ECG, or EKG) features using a convolutional neural network (CNN) to establish an ECG-assisted diagnosis model. Methods: Coal workers who underwent physical examinations at Gequan Mine Hospital and Dongpang Mine Hospital of Hebei Jizhong Energy from July 2020 to September 2020 were selected as the study subjects. The ECG images were preprocessed. We use Python software and convolutional neural network to establish ECG images recognition and classification model.We usecalibration curve, calibration-in-the-large, Brier score, specificity, sensitivity, F1 score, Kappa value, accuracy, and area under the curve (AUC) of ROC to evaluate the performance of the model. Results: The number of abnormal ECG results was 849, and the rate of abnormal results was 25.02%. The test set accuracies of the sinus bradycardia model, nonspecific intraventricular conduction delay model, myocardial ischemia model, and sinus tachycardia model were 97.66%, 96.49%, 93.62%, and 93.02%, respectively; sensitivities were 96.63%, 96.30%, 96.88% and 95.24%, respectively; specificities were 98.78%, 96.67%, 86.67%, and 90.90%, respectively; Brier scores were 0.03, 0.07, 0.09, and 0.11, respectively; Calibration-in-the-large values were 0.026, 0.110, 0.041, and 0.098, respectively. Conclusions: The convolutional neural network model can accurately identify the main ECG abnormality types of coal workers. Additionally, the main ECG abnormalities in these coal company workers were sinus bradycardia, non-specific intraventricular conduction delay, myocardial ischemia, and sinus tachycardia.

Accurate prediction of the price of used vehicles can effectively reduce the undesirable behavior of intermediary platforms that mark up prices indiscriminately, make the link of vehicle sales more transparent and fair, reduce the losses of buyers and sellers, and have great economic significance. However, the previous used vehicle price prediction model suffers from redundant and noisy explicit features, resulting in its poor learning efficiency and prediction ability. Moreover, the previous models did not consider the interaction information among features, resulting in the model missing the acquisition of this information during the learning process, which determines its poor generalization effect during the prediction stage. Therefore, to overcome the shortcomings of previous models, we propose a novel used vehicle price prediction model with denoising autoencoder based on convolution operations (DAECO). Firstly, the DAECO model extracts the latent features of used vehicles using denoising autoencoder to remove the non-discriminatory redundant features. Then the DAECO model uses convolution operation to obtain the interaction information between the latent features. Finally, the DAECO model adds up the interaction information between the latent features and the linear combination information by assigning a certain weight to each of them to obtain the final prediction value. The values of the DAECO model on two real used vehicle transaction datasets with MSE, RMSE and MAE metrics are 1.523 and 2.278, 1.234 and 1.509, 1.009 and 1.226, respectively. Through the comprehensive experimental results on the real used vehicle price dataset, the DAECO model outperforms the current popular baseline algorithms.

Obesity has been an escalating worldwide health problem for decades, and it is likely a risk factor of prediabetes and diabetes. Correlated with obesity, the number of diabetic patients is also remarkable. A modest weight loss (5–10%) is critical to alleviate the risk of any other metabolic disease. Reduced energy intake has been an essential factor for weight loss reduction. As a new behavior intervention to lose weight, intermittent fasting (IF) attracts considerable attention and has become a popular strategy among young people. IF is a diet pattern that cycles between periods of fasting and eating on a regular schedule, involving various types, mainly Intermittent Energy Restriction and Time-Restricted Fasting. Accumulating evidence shows that short-term IF has a greatly positive effect in animal studies and contributes favorable benefits in human trials as well. Nevertheless, as an emerging, diverse, and relatively premature behavior intervention, there are still limited studies considering patients with obesity and type 2 diabetes mellitus. It is also a controversial intervention for the treatment of metabolic disease and cancer. The risks and challenges appear consequently. Additionally, whether intermittent fasting can be applied to long-term clinical treatment, and whether it has side effects during the long-term period or not, demands more large-scale and long-term experiments.