Air pollution and diabetes mellitus: Association and validation in a desert area in China.

BACKGROUND: Despite the growing evidence pointing to the detrimental effects of air pollution on diabetes mellitus (DM), the relationship remains poorly explored, especially in desert-adjacent areas characterized by high aridity and pollution. METHODS: We conducted a cross-sectional study with health examination data from over 2.9 million adults in two regions situated in the southern part of the Taklamakan Desert, China. We assessed three-year average concentrations (2018-2020) of particulate matter (PM1, PM2.5, and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through a space-time extra-trees model. After adjusting for various covariates, we employed generalized linear mixed models to evaluate the association between exposure to air pollutants and DM. RESULTS: The odds ratios for DM associated with a 10 µg/m3 increase in PM1, PM2.5, PM10, CO, and NO2 were 1.898 (95% CI: 1.741, 2.070), 1.07 (95% CI: 1.053, 1.086), 1.013 (95% CI: 1.008, 1.018), 1.009 (95% CI: 1.007, 1.011), and 1.337 (95% CI: 1.234, 1.449), respectively. Notably, men, individuals aged ≥50 years, those with lower educational attainment, nonsmokers, and those not engaging in physical exercise displayed more susceptible to the adverse effects of air pollution. Multiple sensitivity analyses confirmed the stability of these findings. CONCLUSIONS: Our study provides robust evidence of a correlation between prolonged exposure to air pollution and the prevalence of DM among individuals living in the desert-adjacent areas. This research contributes to the expanding knowledge on the relationship between air pollution exposure and DM prevalence in desert-adjacent areas.

Exposure to ambient air pollution and metabolic dysfunction-associated fatty liver disease: Findings from over 2.7 million adults in Northwestern China.

Ambient air pollutants exposures may lead to aggravated Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD). However, there is still a scarcity of empirical studies that have rigorously estimated this association, especially in regions where air pollution is severe. To fill in the literature gap, we conducted a cross-sectional study involving 2711,207 adults living in five regions of southern Xinjiang Uyghur Autonomous Region in 2021. Using a Space-Time Extra-Trees model, we assessed the four-year (2017-2020) average concentrations of particulate matter with aerodynamic diameter ≤1 µm (PM1), particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), particulate matter with aerodynamic diameter ≤10 µm (PM10), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO), and then assigned these values to the participants. Generalized linear mixed models were employed to examine the relationships between air pollutants and the prevalence of MAFLD, with adjustment for multiple confounding factors. The odds ratios and 95% confidence intervals of MAFLD were 2.002 (1.826-2.195), 1.133 (1.108-1.157), 1.034 (1.027-1.040), 1.077 (1.023-1.134), 2.703 (2.322-3.146) and 1.033 (1.029-1.036) per 10 µg/m3 increase in the 4-year average PM1, PM2.5, PM10, O3, SO2 and CO exposures, respectively. The robustness of the findings was confirmed by a series of sensitivities. In summary, long-term exposure to ambient air pollutants was associated with increased odds of MAFLD, particularly in males and individuals with unhealthy lifestyles.

Severer air pollution, poorer cognitive function: Findings from 176,345 elders in Northwestern China.

BACKGROUND: Limited evidence exists regarding the link between air pollution exposure and cognitive function in developing countries, particularly in areas with abundant natural sources of particulate matter. OBJECTIVES: To investigate this association in a large representative sample of the elderly in northwestern China. METHODS: We performed a cross-sectional study among 176,345 participants aged 60-100 years in northwestern China in 2020. A satellite-based spatiotemporal model was applied to assess three-year annual averages of particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5), ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) at residential address. Poor cognitive function was assessed using the Mini-Mental State Examination (MMSE). Generalized linear mixed models were used to assess associations. RESULTS: Compared with participants with the lowest quartiles of PM2.5, PM10, and O3 levels, those with the second, third, and highest quartiles of air pollutants consistently showed increased odds of poor cognitive function and decreased MMSE scores. The odds ratios of poor cognitive function associated with a 10 µg/m3 increment in PM2.5, PM10, and O3 were 1.26 (95 % confidence interval [CI]: 1.17, 1.36), 1.06 (95 %CI: 1.04, 1.08), and 2.76 (95 %CI: 2.11, 3.62), respectively. Subgroup analyses suggested stronger associations between air pollution exposures and poor cognitive function among participants who were younger, were non-Uyghur and were physically active. CONCLUSION: Long-term exposures to PM2.5, PM10 and O3 were associated with poor cognitive function in elders. Our results suggest that reducing air pollution may alleviate the burden of poor cognitive function in the elderly.

Machine learning for predicting diabetes risk in western China adults.

OBJECTIVE: Diabetes mellitus is a global epidemic disease. Long-time exposure of patients to hyperglycemia can lead to various type of chronic tissue damage. Early diagnosis of and screening for diabetes are crucial to population health. METHODS: We collected the national physical examination data in Xinjiang, China, in 2020 (a total of more than 4 million people). Three types of physical examination indices were analyzed: questionnaire, routine physical examination and laboratory values. Integrated learning, deep learning and logistic regression methods were used to establish a risk model for type-2 diabetes mellitus. In addition, to improve the convenience and flexibility of the model, a diabetes risk score card was established based on logistic regression to assess the risk of the population. RESULTS: An XGBoost-based risk prediction model outperformed the other five risk assessment algorithms. The AUC of the model was 0.9122. Based on the feature importance ranking map, we found that hypertension, fasting blood glucose, age, coronary heart disease, ethnicity, parental diabetes mellitus, triglycerides, waist circumference, total cholesterol, and body mass index were the most important features of the risk prediction model for type-2 diabetes. CONCLUSIONS: This study established a diabetes risk assessment model based on multiple ethnicities, a large sample and many indices, and classified the diabetes risk of the population, thus providing a new forecast tool for the screening of patients and providing information on diabetes prevention for healthy populations.

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction.

Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the effective properties of electroencephalograms (EEGs) may not be fully evaluated. To solve this problem, we propose a spatiotemporal graph attention network (STGAT) based on synchronization. The spatial and functional connectivity information between EEG channels was extracted by using the phase locking values (PLVs) first, which allowed multichannel EEG signals to be modeled as graph signals. Afterward, the STGAT model was used to dynamically learn the temporal correlation properties of EEG sequences and explore the spatial topological structure information of multiple channels. Experimental results demonstrated that the STGAT model was able to obtain spatiotemporal correlations and achieve good results on two benchmark datasets. The accuracy, specificity and sensitivity were 98.74%, 99.21% and 98.87%, respectively, on the CHB-MIT dataset. Moreover, all evaluation indices of the private dataset had reached more than 98.8%, with the area under the curve (AUC) reaching 99.96%. The proposed method is superior or comparable to the state-of-the-art models. Extensive experiments demonstrate that our end-to-end automatic seizure prediction model can be extended to design clinical assistant decision systems.

Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants.

Objective: To develop an optimal screening model to identify the individuals with a high risk of hypertension in China by comparing tree-based machine learning models, such as classification and regression tree, random forest, adaboost with a decision tree, extreme gradient boosting decision tree, and other machine learning models like an artificial neural network, naive Bayes, and traditional logistic regression models. Methods: A total of 4,287,407 adults participating in the national physical examination were included in the study. Features were selected using the least absolute shrinkage and selection operator regression. The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in combination with the selected features. The tree-based machine learning models, other machine learning models, and traditional logistic regression models were constructed to identify individuals with hypertension, respectively. Top features selected using the best algorithm and the corresponding variable importance score were visualized. Results: A total of 24 variables were finally included for analyses after the least absolute shrinkage and selection operator regression model. The sample size of hypertensive patients in the training set was expanded from 689,025 to 2,312,160 using the borderline synthetic minority over-sampling technique algorithm. The extreme gradient boosting decision tree algorithm showed the best results (area under the receiver operating characteristic curve of non-laboratory: 0.893 and area under the receiver operating characteristic curve of semi-laboratory: 0.894). This study found that age, systolic blood pressure, waist circumference, diastolic blood pressure, albumin, drinking frequency, electrocardiogram, ethnicity (uyghur, hui, and other), body mass index, sex (female), exercise frequency, diabetes mellitus, and total bilirubin are important factors reflecting hypertension. Besides, some algorithms included in the semi-laboratory analyses showed less improvement in the predictive performance compared to the non-laboratory analyses. Conclusion: Using multiple methods, a more significant prediction model can be built, which discovers risk factors and provides new insights into the prediction and prevention of hypertension.

[Establishment and Application of Foshan Ozone Concentration Forecast Equation].

The formation and changes of ozone (O3), a secondary pollutant in the atmosphere, are complex, and ozone forecasting has become one of the current problems in air pollution prevention and control. In this study, the relationships between the near-surface O3 concentration and meteorological elements (high- and low-level) in Foshan from 2014 to 2017 were analyzed, and the concentration forecasting equation was established, tested, and applied. The results showed that the near-surface O3 changed closely related to high- and low-level meteorological elements. Meteorological elements such as temperature and sunshine hours were significantly positively correlated with O3 concentration, whereas relative humidity, total (low) cloud cover, and wind speed were negatively correlated with O3. Heavy O3 pollution often occurred with meteorological conditions of low wind speed, sunny days and few clouds, low relative humidity, longer sunshine time, and higher temperature. The definitions of high-concentration O3 potential index (HOPI) and wind direction index (WDI) in the Foshan area could better characterize the meteorological conditions of O3 pollution. Considering 13 meteorological elements, such as HOPI and WDI at different heights, the O3 concentration forecasting equation in the Foshan area was established using multi-indicator stacking and multiple stepwise regression methods. Using the 2018 data, it was found that the correlation coefficient R between the simulated values and the measured values reached 0.82, and the forecast equation had a good fitting effect and predictability.

Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN.

BACKGROUND: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists' reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed. METHODS: In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture's traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results. RESULTS: On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance. CONCLUSION: The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG.

Spatiotemporal evolution of epileptic seizure based on mutual information and dynamic brain network.

BACKGROUND: Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. METHODS: In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. RESULTS: Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. CONCLUSION: In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.

Multi-label classification of fundus images based on graph convolutional network.

BACKGROUND: Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in current research. METHODS: This study proposed a multi-label classification method based on the graph convolutional network (GCN), so as to detect 8 types of fundus lesions in color fundus images. We collected 7459 fundus images (1887 left eyes, 1966 right eyes) from 2282 patients (1283 women, 999 men), and labeled 8 types of lesions, laser scars, drusen, cup disc ratio ([Formula: see text]), hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates. We constructed a specialized corpus of the related fundus lesions. A multi-label classification algorithm for fundus images was proposed based on the corpus, and the collected data were trained. RESULTS: The average overall F1 Score (OF1) and the average per-class F1 Score (CF1) of the model were 0.808 and 0.792 respectively. The area under the ROC curve (AUC) of our proposed model reached 0.986, 0.954, 0.946, 0.957, 0.952, 0.889, 0.937 and 0.926 for detecting laser scars, drusen, cup disc ratio, hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates, respectively. CONCLUSIONS: Our results demonstrated that our proposed model can detect a variety of lesions in the color images of the fundus, which lays a foundation for assisting doctors in diagnosis and makes it possible to carry out rapid and efficient large-scale screening of fundus lesions.