Exploring the Impact of COVID-19 on the Air Quality in China (preprint)

At the end of 2019, the COVID-19 emerged in Wuhan, China. It has since put global public health institutions on high alert. People in China reduced their traveling, and production has stopped nationwide during the height of the epidemic. This study explores the effects of these COVID-19-derived changes on air quality in China. Air quality data of 367 cities around China were analyzed. The daily air quality index and air pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) were collected and compared the epidemic period (23.1.2020-23.3.2020) with the preceding two months (22.11.2019-22.1.2020) and the parallel period the year before (23.1.2019-23.3.2019).To compare, we calculated the daily average number of cities with pollution, and the trend in air quality index change. The air quality in the 50 cities with the highest number of confirmed COVID-19 cases and Wuhan was also analyzed. During the period between 23.1.2020 and 23.3.2020, the number of cities with excellent air quality was significantly higher than that in the other two periods. The concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 decreased significantly during this period. The most significant decreases were in PM10 and NO2. The number of cities with good air quality in the later period was significantly higher than a year before. The air quality has improved significantly during the COVID-19 outbreak. The reason for this change might be changes in human activities such as reduced transportation and production stoppage.

Air Quality During The COVID-19 Epidemic in China (preprint)

At the end of 2019, the COVID-19 emerged in Wuhan, China. It has since put global public health institutions on high alert. People reduced their traveling, and production has stopped nationwide during the epidemic. This paper explores the effect of these COVID-19-derived changes on the air quality in China. Air quality data of 367 cities around China were included. The daily air pollutants concentration (AQI,CO, O3, NO2, SO2, PM10, and PM2.5) were collected. We compared the air quality changes between three periods (23.1.2019-23.3.2019, 22.11.2019-22.1.2020, and 23.1.2020–23.3.2020). To compare, we calculated the daily average number of cities with pollution, and the trend in air quality index change. Furthermore, Air quality in the top 50 cities with confirmed cases and Wuhan was analyzed. During the period between 23.1.2020 and 23.3.2020, the number of cities with excellent air quality was significantly higher than that in another two periods. The concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 decreased significantly during the COVID-19 epidemic. The most significant decreases were in PM10 and NO2. The number of cities with good air quality in the later period was significantly higher than that a year before. The air quality has improved significantly during the COVID-19 outbreak, The reason for this change may be human activities such as reduced transportation and production stoppage.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence (preprint)

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Molecular basis for kinin selectivity and activation of the human bradykinin receptors (preprint)

Bradykinin and kallidin are endogenous kinin peptide hormones that belong to the kallikrein-kinin system and are essential to the regulation of blood pressure, inflammation, coagulation, and pain control. Des-Arg 10 -kallidin, the carboxy-terminal des-Arg metabolite of kallidin, and bradykinin selectively activate two G protein-coupled receptors, type 1 and type 2 bradykinin receptors (B1R and B2R), respectively. The hyperactivation of bradykinin receptors, termed “bradykinin storm”, is associated with pulmonary edema in COVID-19 patients, suggesting that bradykinin receptors are important targets for COVID-19 intervention. Here we report two G protein complex structures of B1R and B2R bound to des-Arg 10 -kallidin and bradykinin. Combined with functional analysis, our structures reveal the mechanism of ligand selectivity and specific activation of the bradykinin receptor. These findings also provide a framework for guiding drug design targeting bradykinin receptors for the treatment of inflammation, cardiovascular disorders, and COVID-19.

COVID-19 Vaccination Acceptance Among Healthcare Workers and Nonhealthcare Workers in China: A Survey (preprint)

Background: The coronavirus pneumonia is still spreading around the world. Much progress has been made in vaccine development, and vaccination will become an inevitable trend in the fight against this pandemic. However, the public acceptance of COVID-19 vaccination still remains uncertain. Methods: By calculating the sample size of random sampling, anonymous questionnaire was used in Wen Juan Xing survey platform. Multinomial logistic regression analyses were performed to identify the key sociodemographic, cognitive, and attitude associations with samples of healthcare workers and nonhealth care workers. Findings: A total of 2580 respondents have completed the questionnaire, including 1,329 healthcare workers and 1,251 nonhealthcare workers. This study showed that 76.98% of healthcare workers accepted the COVID-19 vaccine, 18.28% workers were hesitant, and 4.74% workers were resistant. Among the nonhealthcare workers, 56.19% workers received the COVID-19 vaccine, 37.57% workers were hesitant, and 6.24% workers were resistant. Among the healthcare workers, compared with vaccine recipients, vaccine-hesitant individuals were more likely to be female (AOR = 1.52, 95% CI: 1.12–2.07); vaccine-resistant individuals were more likely to live in the suburbs (AOR = 2.81, 95% CI: 1.44–3.99) with an income of 10,000 RMB or greater (AOR = 2.00, 95% CI: 1.03–3.90). Among the nonhealthcare workers, vaccine-hesitant individuals were more likely to be female (AOR = 1.66, 95% CI: 1.31–2.11); vaccine-resistant individuals were also more likely to be female (AOR =1.87, 95% CI: 1.16–3.02) and older than 65 years (AOR = 4.96, 95% CI: 1.40–7.62). There are great differences between healthcare workers and nonhealthcare workers in their cognition and attitude towards vaccines. Interpretation: Our study shows that healthcare workers are more willing to be vaccinated than nonhealthcare workers. Current vaccine safety issues continue to be a major factor affecting public acceptance, and to expand vaccine coverage in response to the COVID-19 pandemic, appropriate vaccination strategies and immunization programs are essential, especially for nonhealthcare workers.Funding: Medical and Technology Project of Zhejiang ProvinceDeclaration of Interest: None to declare. Ethical Approval: This study is a nation-wide cross-sectional study in China; the ethics committee ofAffiliated Hospital of Hangzhou Normal University approved all the procedures performed.

A collaborative online AI engine for CT-based COVID-19 diagnosis (preprint)

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.