BACKGROUND. The role of humoral immunity in the coronavirus disease 2019 (COVID-19) is not fully understood owing, in large part, to the complexity of antibodies produced in response to the SARS-CoV-2 infection. There is a pressing need for serology tests to assess patient-specific antibody response and predict clinical outcome. METHODS. Using SARS-CoV-2 proteome and peptide microarrays, we screened 146 COVID-19 patients plasma samples to identify antigens and epitopes. This enabled us to develop a master epitope array and an epitope-specific agglutination assay to gauge antibody responses systematically and with high resolution. RESULTS. We identified linear epitopes from the Spike (S) and Nucleocapsid (N) protein and showed that the epitopes enabled higher resolution antibody profiling than the S or N protein antigen. Specifically, we found that antibody responses to the S(811-825), S(881-895) and N(156-170) epitopes negatively or positively correlated with clinical severity or patient survival. Moreover, we found that the P681H and S235F mutations associated with the coronavirus variant of concern B.1.1.7 altered the specificity of the corresponding epitopes. CONCLUSIONS. Epitope-resolved antibody testing not only affords a high-resolution alternativeto conventional immunoassays to delineate the complex humoral immunity to SARS-CoV-2 and differentiate between neutralizing and non-neutralizing antibodies, it may potentially be used to predict clinical outcome. The epitope peptides can be readily modified to detect antibodies against variants of concern (VOC) in both the peptide array and latex agglutination formats.
We have developed a rapid, accurate, and cost-effective serologic test for SARS-CoV-2 virus, which caused the COVID-19 pandemic, on the basis of antibody-dependent agglutination of antigen-coated latex particles. When validated using plasma samples that are positive or negative for SARS-CoV-2, the agglutination assay detected antibodies against the receptor-binding domain of the spike (S-RBD) or the nucleocapsid protein of SARS-CoV-2 with 100% specificity and ∼98% sensitivity. Furthermore, we found that the strength of the S-RBD antibody response measured by the agglutination assay correlated with the efficiency of the plasma in blocking RBD binding to the angiotensin-converting enzyme 2 in a surrogate neutralization assay, suggesting that the agglutination assay might be used to identify individuals with virus-neutralizing antibodies. Intriguingly, we found that >92% of patients had detectable antibodies on the day of a positive viral RNA test, suggesting that the agglutination antibody test might complement RNA testing for the diagnosis of SARS-CoV-2 infection.
The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases. Currently, the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues. The aim of this article is graph-based modelling of the COVID-19 infection spread. The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics. We propose a conceptual model of COVID-19 epidemic by considering the social distance, the duration of contact with an infected person and their location-based demographic characteristics. Based on the hypothetical scenario of the spread of the virus, a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19. The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments. The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model. This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly. Note that social, economic, demographic factors, the population density, mental values and etc. affect the increase in number of cases of infection and hence, the research was not able to consider all factors. In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.
The rapid development of ICT has a significant impact on the lifestyle of and communication among people. Such impact tendencies alter the human activity as well as government functions and the ways these are implemented. The studies related to Web 2.0, social media, social networks, and their use in the government sector show that the issues such as the formation of social media and important role of the latter in public administration have become a broad research topic. Despite the presence of various approaches of states to social media and social media analytics in international practice, the large impact of social media on public administration is of no doubt. The chapter reviews such issues in the presence of the goal of building mutual communication between government bodies and citizens, the role of social media in building feedback between e-government and citizens, the use of social media in e-government, and the transformation of administrative mechanisms.
The aim of the study is the application of multi-criteria evaluation methods for ranking of candidates in e-voting. Due to the potential to enhance the electoral efficiency in e-voting multiple criteria, such as personality traits, activity and reputation in social media, opinion followers on election area and so on for the selection of qualified personnel can be considered. In this case, the number of criteria excesses in the decision-making stage directed us to the use of a multi-criteria decision making model (MCDM). This paper proposes MCDM for weighted ranking of candidates in e-voting. Criteria for the candidates' ranking and selection are determined and each voter uses the linguistic scales for the ranking of each candidate. Candidates' ranking is evaluated according to all criteria. In a numerical study, it is provided the candidates' evaluation on the base of selected criteria and ranked according to the importance of criteria. To assess the importance of the criteria and to evaluate the suitability of the candidates for each of the criteria, the voters use linguistic variables. In practice, the proposed model can use different evaluation scales for the selection of candidates in e-voting. The proposed model allows selecting a candidate with the competencies based on the criteria set out in the e-voting process and making more effective decisions.
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