Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O3 and PM2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O3, CO is the most important factor for PM2.5, and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs.

Lymphocyte-monocyte-neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis.

BACKGROUND: It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed. METHODS: Clinical indicators of COVID-19 patients from two independent cohorts (Training data: Hefei Cohort, 82 patients; Validation data: Nanchang Cohort, 169 patients) were retrospected. Sparse principal component analysis (SPCA) using Hefei Cohort was performed and prediction models were deduced. Prediction results were evaluated by receiver operator characteristic curve and decision curve analysis (DCA) in above two cohorts. RESULTS: SPCA using Hefei Cohort revealed that the first 13 principal components (PCs) account for 80.8% of the total variance of original data. The PC1 and PC12 were significantly associated with disease severity with odds ratio of 4.049 and 3.318, respectively. They were used to construct prediction model, named Model-A. In disease severity prediction, Model-A gave the best prediction efficiency with area under curve (AUC) of 0.867 and 0.835 in Hefei and Nanchang Cohort, respectively. Model-A's simplified version, named as LMN index, gave comparable prediction efficiency as classical clinical markers with AUC of 0.837 and 0.800 in training and validation cohort, respectively. According to DCA, Model-A gave slightly better performance than others and LMN index showed similar performance as albumin or neutrophil-to-lymphocyte ratio. CONCLUSIONS: Prediction models produced by SPCA showed robust disease severity prediction efficiency for COVID-19 patients and have the potential for clinical application.

Heavy metals in submicronic particulate matter (PM1) from a Chinese metropolitan city predicted by machine learning models.

The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 µm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 µg/m3 (range: 7.00-73.40 µg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.

Infection with SARS-CoV-2 causes abnormal laboratory results of multiple organs in patients.

AIM: To evaluate the clinical value of abnormal laboratory results of multiple organs in patients with coronavirus disease 2019 (COVID-2019) and to help clinicians perform correct treatment. RESULTS: Elevated neutrophil-to-LYM ratio (NLR), D-dimer(D-D), interleukin (IL)-6, IL-10, IL-2, interferon-Y, and age were significantly associated with the severity of illness. However, significant and sustained decreases were observed in the LYM subset (p<0.05). D-D, T cell counts, and cytokine levels in severe COVID-19 patients who survived the disease gradually recovered at later time points to levels that were comparable to those of mild cases. Second, D-D increased from 0.5 to 8, and the risk ratio increased from 2.75 to 55, eventually leading to disseminated intravascular coagulation. Moreover, the acute renal function damage occurred earlier than abnormal heart and liver functions (p<0.05). CONCLUSIONS: The degrees of lymphopenia and proinflammatory cytokine storm were higher in severe COVID-19 patients than in mild cases. The degree was associated with the disease severity. Advanced age, NLR, D-D, and cytokine levels may serve as useful prognostic factors for the early identification of severe COVID-19 cases. METHODS: Peripheral blood samples were collected from 93 confirmed COVID-19 patients. The samples were examined for lymphocyte (LYM) subsets by flow cytometry and cytokine profiles by specific immunoassays. The receiver operating characteristic curve was applied to determine the best diagnostic thresholds for laboratory results, and principal component analysis was used to screen the major risk factors. The prognostic values were assessed using the Kaplan-Meier curve and univariate and multivariate COX regression models.

The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients.

AIM: To accumulate evidence that indicated the key role played by virus-triggered inflammation in the 2019-novel coronavirus disease (COVID-19) which emerged in Wuhan City and rapidly spread throughout China. METHODS: Age, neutrophil(NEU)-to-lymphocyte (LYM) ratio (NLR), lymphocyte-to-monocyte (MON) ratio, platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP) of 93 patients with laboratory confirmed COVID-19 were investigated and compared. The receiver operating characteristic curve was applied to determine the thresholds for five bio-markers, and their prognostic values were assessed via the Kaplan-Meier curve and multivariate COX regression models. RESULTS: The median age was 46.4 years old, and 37cases were females. A total of 27.8% of patients had been to Wuhan, and 73.1% had contacted with people from Wuhan. Fever (83.8%) and cough (70.9%) were the two most common symptoms. Elevated NLR and age were significantly associated with illness severity. The binary logistic analysis identified elevated NLR (hazard risk [HR] 2.46, 95% confidence interval [CI] 1.98-4.57) and age (HR 2.52, 95% CI 1.65-4.83) as independent factors for poor clinical outcome of COVID-19. NLR exhibited the largest area under the curve at 0.841, with the highest specificity (63.6%) and sensitivity (88%). CONCLUSIONS: Elevated age and NLR can be considered independent biomarkers for indicating poor clinical outcomes.