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Cell ; 185(13): 2265-2278.e14, 2022 06 23.
Article in English | MEDLINE | ID: covidwho-1803705


Breakthrough infections by SARS-CoV-2 variants become the global challenge for pandemic control. Previously, we developed the protein subunit vaccine ZF2001 based on the dimeric receptor-binding domain (RBD) of prototype SARS-CoV-2. Here, we developed a chimeric RBD-dimer vaccine approach to adapt SARS-CoV-2 variants. A prototype-Beta chimeric RBD-dimer was first designed to adapt the resistant Beta variant. Compared with its homotypic forms, the chimeric vaccine elicited broader sera neutralization of variants and conferred better protection in mice. The protection of the chimeric vaccine was further verified in macaques. This approach was generalized to develop Delta-Omicron chimeric RBD-dimer to adapt the currently prevalent variants. Again, the chimeric vaccine elicited broader sera neutralization of SARS-CoV-2 variants and conferred better protection against challenge by either Delta or Omicron SARS-CoV-2 in mice. The chimeric approach is applicable for rapid updating of immunogens, and our data supported the use of variant-adapted multivalent vaccine against circulating and emerging variants.

COVID-19 , Vaccines , Animals , Antibodies, Neutralizing , Antibodies, Viral , COVID-19/prevention & control , COVID-19 Vaccines , Humans , Mice , SARS-CoV-2/genetics
J Ambient Intell Humaniz Comput ; : 1-13, 2021 Apr 09.
Article in English | MEDLINE | ID: covidwho-1182326


Through the COVID-19 epidemic in 2020, the society has deeply realized the inevitability and necessity of building a community that shares the future of mankind. In the face of severely complex international trends and domestic and international economic conditions, artificial intelligence plays an important auxiliary role in the regular prevention and management of COVID-19. In order to effectively correspond to the formalized extensional prevention and control theory, it is essential to use coordination models, rule systems, prevention and control mechanisms, and governance landscapes to build artificial intelligence corresponding systems. This article uses a basic genetic algorithm to realize the robot path plan. This mainly includes the establishment of environmental models, the discovery of chromosomes and the determination of coding methods, the selection and design of fitness functions, and related designs. This paper proposes a new adaptive adjustment mode based on the basic genetic algorithm, which improves the selection and mutation operation, and improves the optimization efficiency of the genetic algorithm. Building an artificial intelligence response system may face various technical risks and governance dilemmas. Only by improving the rule system of artificial intelligence, creating an epidemic prevention and control ecology, conserving the public spirit of the whole people, strengthening the governance of the source of crisis, and further improving the new momentum of economic and social development and public safety. The modernization of governance capabilities can better respond to the current complex situation.

Pers Ubiquitous Comput ; : 1-10, 2021 Feb 04.
Article in English | MEDLINE | ID: covidwho-1074427


The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence-assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence-assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.