ABSTRACT
There is not yet sufficient evidence that autoimmune disease (AID) increases susceptibility to coronavirus disease 2019 (COVID-19);however, AID can induce organ damage, cardiovascular and respiratory disease, and the use of multiple immunosuppressants in treatment may increase the risk of adverse outcomes following COVID-19 infection in patients with AID. Prevention strategies for COVID-19 should be prioritized in patients with AID, and COVID-19 vaccination may be a useful route. The benefits of COVID-19 vaccination in AID patients with stable disease activity outweigh the potential risks. The mRNA vaccine, inactivated vaccine and recombinant protein subunit vaccine are currently recommended for patients with AID, but recombinant adenoviral vector vaccines should be administered only when the benefits outweigh the risks in a comprehensive assessment, and live attenuated vaccines should be avoided. Most AID-therapeutic drugs have little effect on the immune response to COVID-19 vaccines and can be used normally during the vaccination period, while some drugs, such as methotrexate and rituximab, may reduce the immune response to COVID-19 vaccine and the timing of vaccination should be adjusted. This article discusses the necessity, safety and precautions of COVID-19 vaccination in patients with AID based on the existing clinical guidelines, expert consensus and literature studies.
ABSTRACT
Recently, COVID-19 has become one of the most severe and widespread diseases with an increasing number of infections and deaths. An accurate and high-speed automatic classifier will increase the efficiency of diagnosis and reduce fatigue misdiagnosis. Given the contradiction that many previous classifiers require a large amount of data for training while it is difficult to collect the medical images of COVID-19 with labels, we propose a classification model based on self-supervised learning and transfer learning, which uses rotation and division as labels and then transfers the parameters to the classifier. It solves the overfitting problem caused by insufficient data set and improves the accuracy by nearly 30% © 2021 ACM.
ABSTRACT
Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO2 column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NOX) emissions and quantify the contributions of local emissions and regional transport to NOx in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NOX emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NOX from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R-2 = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NOX emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NOX concentration in BJ;(2) in W_2019, NOX emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018;(3) in W_2019, NOX influx substantially decreased (-699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.