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Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics
Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vacc...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239437/ https://www.ncbi.nlm.nih.gov/pubmed/34211454 http://dx.doi.org/10.3389/fimmu.2021.574411 |
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author | Natali, Eriberto N. Babrak, Lmar M. Miho, Enkelejda |
author_facet | Natali, Eriberto N. Babrak, Lmar M. Miho, Enkelejda |
author_sort | Natali, Eriberto N. |
collection | PubMed |
description | Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1–5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research. |
format | Online Article Text |
id | pubmed-8239437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82394372021-06-30 Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics Natali, Eriberto N. Babrak, Lmar M. Miho, Enkelejda Front Immunol Immunology Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1–5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research. Frontiers Media S.A. 2021-06-15 /pmc/articles/PMC8239437/ /pubmed/34211454 http://dx.doi.org/10.3389/fimmu.2021.574411 Text en Copyright © 2021 Natali, Babrak and Miho https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Natali, Eriberto N. Babrak, Lmar M. Miho, Enkelejda Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics |
title | Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics |
title_full | Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics |
title_fullStr | Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics |
title_full_unstemmed | Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics |
title_short | Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics |
title_sort | prospective artificial intelligence to dissect the dengue immune response and discover therapeutics |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239437/ https://www.ncbi.nlm.nih.gov/pubmed/34211454 http://dx.doi.org/10.3389/fimmu.2021.574411 |
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