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Gut Microbes ; 14(1): 2117503, 2022.
Article in English | MEDLINE | ID: covidwho-2028942


The origins of preexisting SARS-CoV-2 cross-reactive antibodies and their potential impacts on vaccine efficacy have not been fully clarified. In this study, we demonstrated that S2 was the prevailing target of the preexisting S protein cross-reactive antibodies in both healthy human and SPF mice. A dominant antibody epitope was identified on the connector domain of S2 (1147-SFKEELDKYFKNHT-1160, P144), which could be recognized by preexisting antibodies in both human and mouse. Through metagenomic sequencing and fecal bacteria transplant, we demonstrated that the generation of S2 cross-reactive antibodies was associated with commensal gut bacteria. Furthermore, six P144 reactive monoclonal antibodies were isolated from naïve SPF mice and were proven to cross-react with commensal gut bacteria collected from both human and mouse. A variety of cross-reactive microbial proteins were identified using LC-MS, of which E. coli derived HSP60 and HSP70 proteins were confirmed to be able to bind to one of the isolated monoclonal antibodies. Mice with high levels of preexisting S2 cross-reactive antibodies mounted higher S protein specific binding antibodies, especially against S2, after being immunized with a SARS-CoV-2 S DNA vaccine. Similarly, we found that levels of preexisting S2 and P144-specific antibodies correlated positively with RBD binding antibody titers after two doses of inactivated SARS-CoV-2 vaccination in human. Collectively, our study revealed an alternative origin of preexisting S2-targeted antibodies and disclosed a previously neglected aspect of the impact of gut microbiota on host anti-SARS-CoV-2 immunity.

COVID-19 , Gastrointestinal Microbiome , Viral Vaccines , Animals , Antibodies, Monoclonal , Antibodies, Viral , COVID-19/prevention & control , COVID-19 Vaccines , Escherichia coli , Humans , Mice , SARS-CoV-2
Cities ; 122: 103472, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1565531


The coronavirus disease (COVID-19) outbreak has immensely changed people's travel behaviour. The changes in travel behaviour have had a huge impact on different industries, such as consumption, entertainment, commerce, office, and education. This study investigates the impact of COVID-19 on population travel patterns from three aspects: total trips, travel recovery degree, and travel distance. The result indicates that COVID-19 has reduced the total number of cross-city trips and flexible non-work travel; in the post-pandemic era, cross-city travel is mainly short-distance (distance <100 km). This study has significant policymaking implications for governments in countries where the population shares a similar change in travel behaviour.

Journal of Physics: Conference Series ; 1982(1), 2021.
Article in English | ProQuest Central | ID: covidwho-1338611


In 2020, SARS-CoV-2 will affect the hearts of people all over the country, and Weibo will become the representative of people expressing their feelings on the Internet. Traditional emotion dictionary and machine learning methods have poor text emotion recognition effect, while BERT pre-training model is based on bidirectional Transformer model, which can better obtain the emotion expressed by the text and effectively improve the accuracy of the model. On the basis of improving BERT pre-training model, attention mechanism is introduced, and the key features are weighted to make emotion classification more accurate. According to the analysis of emotions expressed by netizens on Weibo during the epidemic, compared with textCNN model, BILSTM model and BILSTM+Attention model, the accuracy rate has increased by 6.25%, 4.69% and 2.67% respectively. The overall performance of this model is the best, and it can effectively recognize text emotion.