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Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach. An outcome prediction alternative
dc.creator | Castro Castro, Ana Cristina | |
dc.creator | Figueroa Protti, Lucía | |
dc.creator | Molina Mora, José Arturo | |
dc.creator | Rojas Salas, María Paula | |
dc.creator | Villafuerte Mena, Danae | |
dc.creator | Suárez Sánchez, María José | |
dc.creator | Sanabria Castro, Alfredo | |
dc.creator | Boza Calvo, Carolina | |
dc.creator | Calvo Flores, Leonardo | |
dc.creator | Solano Vargas, Mariela | |
dc.creator | Madrigal Sánchez, Juan José | |
dc.creator | Sibaja Campos, Mario | |
dc.creator | Silesky Jiménez, Juan Ignacio | |
dc.creator | Chaverri Fernández, José Miguel | |
dc.creator | Soto Rodríguez, Mario Andrés | |
dc.creator | Echeverri McCandless, Ann | |
dc.creator | Rojas Chaves, Sebastián | |
dc.creator | Landaverde Recinos, Denis | |
dc.creator | Weigert, Andreas | |
dc.creator | Mora Rodríguez, Javier Francisco | |
dc.date.accessioned | 2022-09-02T19:29:10Z | |
dc.date.available | 2022-09-02T19:29:10Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | https://www.frontiersin.org/journals/medicine | es_ES |
dc.identifier.uri | https://hdl.handle.net/10669/87283 | |
dc.description.abstract | COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV2 infection. | es_ES |
dc.description.sponsorship | Universidad de Costa Rica/[807-C1-357]/UCR/Costa Rica | es_ES |
dc.language.iso | eng | es_ES |
dc.source | Frontiers in Medicine, pp.1-10. | es_ES |
dc.subject | COVID-19 | es_ES |
dc.subject | SARS-CoV2 | es_ES |
dc.subject | CXCL10 | es_ES |
dc.subject | IL-38 | es_ES |
dc.subject | Cytokine profile | es_ES |
dc.title | Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach. An outcome prediction alternative | es_ES |
dc.type | artículo original | es_ES |
dc.identifier.doi | 10.3389/fmed.2022.987182 | |
dc.description.procedence | UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Hematología y Trastornos Afines (CIHATA) | es_ES |
dc.description.procedence | UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET) | es_ES |
dc.description.procedence | UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Cirugía y Cáncer (CICICA) | es_ES |
dc.identifier.codproyecto | 807-C1-357 |