SARS-CoV-2 E protein: Pathogenesis and potential therapeutic development.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a devastating global pandemic, which has seriously affected human health worldwide. The discovery of therapeutic agents is extremely urgent, and the viral structural proteins are particularly important as potential drug targets. SARS-CoV-2 envelope (E) protein is one of the main structural proteins of the virus, which is involved in multiple processes of the virus life cycle and is directly related to pathogenesis process. In this review, we present the amino acid sequence of the E protein and compare it with other two human coronaviruses. We then explored the role of E protein in the viral life cycle and discussed the pathogenic mechanisms that E protein may be involved in. Next, we summarize the potential drugs against E protein discovered in the current studies. Finally, we described the possible effects of E protein mutation on virus and host. This established a knowledge system of E protein to date, aiming to provide theoretical insights for mitigating the current COVID-19 pandemic and potential future coronavirus outbreaks.

Analysis of the attention to COVID-19 epidemic based on visibility graph network

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level. [ABSTRACT FROM AUTHOR] Copyright of Modern Physics Letters B is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)