Juxiang Yuan

Objectives: Exposure to light at night (LAN) can disturb circadian endocrine and metabolic rhythms. Hyperuricemia (HUA) is an early-onset metabolic disorder. However, it is still not clear whether LAN exposure increases the prevalence of HUA. Material and Methods: The authors used...

The misalignment between the circadian clock and behavioral cycles has been implicated in pathogenesis of many diseases. The main purpose of this study is to examine the association between rotating night shift work, exposure to light at night, and glomerular filtration rate among steelworkers in north China. A total of 6869 steelworkers, aged 22 to 60 years, were included in this study. Multivariable logistic regression was used to examine the association between night shift work, the brightness of bedroom ambient light at night (LAN), and estimated glomerular filtration rate (eGFR), with adjustment for potential confounders. Mediation analysis was performed to examine the mediation effect of potential mediators on the association of duration of night shifts and eGFR. Long duration of night shift work (≥29 years) had elevated odds of decreased eGFR (≤89 mL/min/1.73 m²) (odds ratio (OR), 1.37, 95% confidence interval (CI) 1.09–1.73) compared with day work after adjustment for potential confounders. The association between duration of night shifts and eGFR (continuous) was partially modified by diastolic blood pressure (average causal mediation effect (ACME), –0.077, 95% CI –0.134 to −0.030, p < 0.001). No significant associations were observed among the different brightness of bedroom ambient light levels: middle level (OR, 0.90, 95% CI 0.77–1.05), lightest level (OR, 0.94, 95% CI 0.75–1.18), and decreased eGFR compared with the darkest level. Long-term night-shift work, rather than the brightness of bedroom ambient LAN, is associated with early stage of renal dysfunction in steelworkers, and blood pressure may mediate the relationship between night shift work and decreased eGFR.

The convolutional neural network (CNN) has made certain progress in image processing, language processing, medical information processing and other aspects, and there are few relevant researches on its application in disease risk prediction. Dyslipidemia is a major and modifiable risk factor for cardiovascular disease, early detection of dyslipidemia and early intervention can effectively reduce the occurrence of cardiovascular diseases. Risk prediction model can effectively identify high-risk groups and is widely used in public health and clinical medicine. Steel workers are a special occupational group. Their particular occupational hazards, such as high temperatures, noise and shift work, make them more susceptible to disease than the general population, which makes the risk prediction model for the general population no longer applicable to steel workers. Therefore, it is necessary to establish a new model dedicated to the prediction of dyslipidemia of steel workers. In this study, the physical examination information of thousands of steel workers was collected, and the risk factors of dyslipidemia in steel workers were screened out. Then, based on the data characteristics, the corresponding parameters were set for the convolutional neural network model, and the risk of dyslipidemia in steel workers was predicted by using convolutional neural network. Finally, the predictive performance of the convolutional neural network model is compared with the existing predictive models of dyslipidemia, logistics regression model and BP neural network model. The results show that the convolutional neural network has a good predictive performance in the risk prediction of dyslipidemia of steel workers, and is superior to the Logistic regression model and BP neural network model.

Acquired Immune Deficiency Syndrome (AIDS) is still one of the most life-threatening diseases in the world. Moreover, new infections are still potentially increasing. This difficult problem must be solved. Early warning is the most effective way to solve this problem. Here, we aim to determine the best performing model to track the epidemic of AIDS, which will provide a methodological basis for testing the time characteristics of the disease. From January 2004 to January 2018, we built four computing methods based on AIDS dataset: BPNN model, RNN model, LSTM model and MHPSO-GRU model. Compare the final estimated performance to determine the preferred method. Result. Considering the root mean square error (RMSE), mean absolute error (MAE), mean error rate (MER) and mean absolute percentage error (MAPE) in the simulation and prediction subsets, the MHPSO-GRU model is determined as the best performance technology. Estimates for the period from May 2018 to December 2020 suggest that the event appears to continue to increase and remain high.

Aiming at the problem that the petroleum industry is a high-occurrence industry with many occupational hazards, in order to comprehensively evaluate the hazards in coal mines, the number of supervisors for petroleum occupational hazards is small, the number of objects to be supervised, the frequency of supervision, and the scope of supervision are large, combined with cloud computing Based on the characteristics of cloud computing, an occupational hazard supervision system for petroleum companies based on cloud computing is proposed. Based on relevant theories, 15 occupational hazard identification indicators such as lifestyle, average working age, cultural level, professional skills, and health level were selected from massive data to establish an occupational hazard evaluation index system. In this paper, the analytic hierarchy process is used to determine the weight of the occupational hazard identification index, and then a BPNN neural network prediction model is established based on the determined weight index system. The qualitative prediction of the input and output of the index is achieved through model solving and model training. The experimental results show that the training error accuracy of BPNN neural network is less than 5%, and the model accuracy meets the requirements. Compared with the error of AHP, the average evaluation error of BPNN is 0.637% lower than that of AHP. The comprehensive evaluation system based on BPNN accords with the actual situation of the enterprise is greater than 97%, so it has certain guiding significance in the evaluation of petroleum occupational diseases.

With the development of medical digitization technology, artificial intelligence and big data technology, the medical model is gradually changing from treatment-oriented to prevention-oriented. In recent years, with the rise of artificial neural networks, especially deep learning, great achievements have been made in realizing image classification, natural language processing, text processing and other fields. Combining artificial intelligence and big data technology for disease risk prediction is a research focus in the field of intelligent medicine. Blood lipids are the main risk factors of cardiovascular and cerebrovascular diseases. If early prediction of abnormal blood lipids in iron and steel workers can be carried out, early intervention can be carried out, which is beneficial to protect the health of iron and steel workers. This paper around the steel workers dyslipidemia prediction problem for further study, firstly analyzes the influence factors of the steel workers dyslipidemia, discusses the commonly used method for prediction of disease, and then studied deep learning related theory, this paper introduces the two deep learning algorithms of RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory). Use the basic principle of Python language and the TensorFlow deep learning framework, establishes a prediction model based on two deep learning networks, and makes an example analysis. Experimental results show the LSTM prediction effect is superior to traditional RNN network, It provides scientific basis for the prevention of iron and steel dyslipidemia.

The study designs a comprehensive evaluation system for the prevention and control of occupational hazards, calculates its weight coefficient, and provides a potential strategic and effective tool for the scientific evaluation of occupational hazards in the iron and steel enterprises. The system was established through induction and analysis of relevant literature, personal interview, theoretical analysis, Delphi expert consultation, and special group discussions. Using an improved analytical hierarchy process fuzzy comprehensive evaluation model and on the basis of the improved Delphi expert investigation, the weight of the operability comprehensive evaluation index system is constructed. A three-level index system is established on the basis of harmful factors of occupational activities, health status of employees, protection facilities of occupational hazards, occupational health management, and so on. The index system structure is 4-20-95, and the weight coefficients of the four dimensions are 0.2516, 0.2428, 0.2550, and 0.2506. The recovery rate of the questionnaire was 82.5%, 100.0%, and 100.0%. The effective rates were 75.0%, 100.0%, and 100.0%. Conversely, the expert authority coefficients of the four dimensions are 0.875, 0.769, 0.832 and 0.800. Results show that the consistency factors of the four dimensions are statistically significant. Cronbach’s α coefficient, standardized Cronbach’s α coefficient, and split-half reliability of the comprehensive evaluation index system are 0.959, 0.950, and 0.810, respectively. After factor analysis, four common factors were extracted on the basis of expert opinions, and the cumulative variance was 63.1%. The comprehensive evaluation system for the prevention and control of occupational hazards in the iron and steel enterprises proposed by the study is relatively complete and reasonable.

The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo‐MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo‐MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo‐MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo‐MDS and AA and analyzes the characteristics of the optimal model misjudgment data.