[Clinical update in non-invasive positive pressure ventilation for respiratory failure in 2022].

In this article, we searched the research literatures related to clinical investigation of non-invasive positive pressure ventilation (NPPV) in acute respiratory failure(ARF)/chronic respiratory failure(CRF) between 1st October 2021 and 30th September 2022 through Medline, and reviewed the important advances. Three prospective randomized controlled studies related to the efficacy and safety of NPPV and/or high-flow nasal cannula oxygen therapy (HFNC) on patients with COVID-19 with ARF were reported, showing that NPPV (including continuous positive airway pressure and bilevel positive airway pressure) was able to reduce the intubation rate, but the efficacy of HFNC was contradictory. In addition, progress has been made in outcome prediction models for ARF treated with NPPV, NPPV-related cardiac arrest, and the impact of human-machine interface on NPPV treatment outcomes. The effects of NPPV as preoxygenation method before intubation was reported to be able to reduce severe desaturation during intubation, especially in obese population. The use of NPPV in extubated patients resulting in reduced reintubation rate was also studied. With regard to long-term home application of NPPV, five indicators of successful initiation were proposed, but the success rate was low in clinical practice. Some reports showed that psychological support could improve the adherence to NPPV. The results of these studies contributed to the rational selection and optimal application of NPPV in clinical practice.

Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network

Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). The annual change in PM2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 mu g/m(3)), and root mean square error (6.6 mu g/m(3)), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables.

Changes in Air Quality from the COVID to the Post-COVID Era in the Beijing-Tianjin-Tangshan Region in China

This article discussed air quality changes in the Beijing-Tianjin-Tangshan (BTT) region. The air quality index (AQI) values, and the concentrations of PM2.5, PM10, SO2, CO, NO2, and O-3 in the BTT region during the COVID-19 outbreak in 2020 were, respectively, 79.4, 47.2 mu g m(-3), 73.4 mu g m(-3), 10.3 mu g m(-3), 0.87 mg m(-3), 33.6 mu g m(-3), and 90.7 mu g m(-3). However, they were, respectively, 102.7, 61.4 mu g m(-3), 121.0 mu g m(-3), 9.0 mu g m(-3), 0.88 mg m(-3), 40.1 mu g m(-3), and 84.0 mu g m(-3) during the same period in 2021, which is an increase of 29.2%, 30.1%, 64.8%, -12.9%, 1.94 %, 19.5%, and -7.4% compared with the values in 2020. The combined proportions of grade I and grade II during the COVID-19 outbreak in 2020 were 16.7% higher than those in the same period in 2021, so the air quality has deteriorated rapidly from 2020 to the post-COVID era in 2021. The possible reasons for poorer air quality are that the frequency of dusty weather and air pollutant discharge has increased, and meteorological conditions have been relatively unfavorable. The average AQI values, and concentrations of PM2.5, PM10, SO2, CO, NO2, and O-3 during the post-COVID period in 2021 respectively decreased by 14.8%, 29.0%, 14.6%, 22.5%, 37.4%, 14.8%, and 8.7%, compared with those in 2020. It is also worth noting that all the changes in air pollution during the post-COVID era have been consistent. The combined proportions of grade I and grade II during post-COVID period in 2021 were 18.4% higher than those during the same period of 2020, which indicates that the air quality during post-COVID 2021 has obviously improved compared with those in the same period of 2020. The possible reasons are a series of clean air policies and clean air actions, as well as favorable atmospheric diffusion conditions. These results indicate that clean air policies play a very important role in improving air quality.