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    Testing a vaccine candidate against Hepatitis C virus designed by combinatorial optimization

    Autor: 
    Malaina, Iker
    ;
    Martinez, Luis
    ;
    Salcines-Cuevas, David
    ;
    Teran-Navarro, Héctor
    ;
    Ocejo-Vinyals, J. Gonzalo
    ;
    Gonzalez-Lopez, Elena
    ;
    Soriano, Vicente
    ;
    Úbeda Cantera, María
    ;
    Pérez Pinilla, Martin-Blas
    ;
    Martínez de la Fuente, Ildefonso
    ;
    Álvarez-Domínguez, Carmen
    Fecha: 
    2023
    Palabra clave: 
    vaccine; Hepatitis C; virus; Scopus; JCR; WOS
    Revista / editorial: 
    Scientific Reports
    Citación: 
    Malaina, I., Martinez, L., Salcines-Cuevas, D. et al. Testing a vaccine candidate against Hepatitis C virus designed by combinatorial optimization. Sci Rep 13, 21746 (2023). https://doi.org/10.1038/s41598-023-48458-x
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/16065
    DOI: 
    https://doi.org/10.1038/s41598-023-48458-x
    Dirección web: 
    https://www.nature.com/articles/s41598-023-48458-x
    Open Access
    Resumen:
    This paper presents a new procedure for vaccine design against highly variable viruses such as Hepatitis C. The procedure uses an optimization algorithm to design vaccines that maximize the coverage of epitopes across different virus variants. Weighted epitopes based on the success ratio of immunological assays are used to prioritize the selection of epitopes for vaccine design. The procedure was successfully applied to design DC vaccines loaded with two HCV peptides, STG and DYP, which were shown to be safe, immunogenic, and able to induce significant levels of anti-viral cytokines, peptide-specific cellular immune responses and IgG antibodies. The procedure could potentially be applied to other highly variable viruses that currently lack effective vaccines.
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    Nombre: Testing_a_vaccine_candidate_against_Hepatitis_C_virus.pdf
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    Formato: application/pdf
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