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Immune-based prediction of COVID-19 severity and chronicity decoded using machine learning

dc.creatorPatterson, Bruce K.
dc.creatorGuevara Coto, José
dc.creatorYogendra, Ram
dc.creatorFrancisco, Edgar B.
dc.creatorLong, Emily
dc.creatorPise, Amruta
dc.creatorRodrigues, Hallison
dc.creatorParikh, Parvi S.
dc.creatorMora Rodríguez, Javier
dc.creatorMora Rodríguez, Rodrigo Antonio
dc.date.accessioned2025-08-25T21:36:56Z
dc.date.issued2021-06-28
dc.description.abstractExpression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of COVID-19. We used a bioinformatics approach to predict and model the immunologic phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 224 individuals including healthy controls and patients spanning the COVID-19 disease continuum. We assessed the plasma and isolated peripheral blood mononuclear cells (PBMCs) from 29 healthy controls, 26 Mild-Moderate COVID-19 individuals, 48 Severe COVID-19 individuals, and 121 individuals with post-acute sequelae of COVID-19 (PASC) symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients from each group. B-cells were significantly elevated compared to healthy control individuals (P<0.001) as was the CD14+, CD16+, CCR5+ monocytic subset (P<0.001). CD4 and CD8 positive T-cells expressing PD-1 as well as T-regulatory cells were significantly lower than healthy controls (P<0.001 and P=0.01 respectively). CCL5/RANTES, IL-2, IL-4, CCL3, IL-6, IL-10, IFN-g, and VEGF were all significantly elevated compared to healthy controls (all P<0.001). Conversely GM-CSF and CCL4 were in significantly lower levels than healthy controls (P=0.01). Data were further analyzed and the classes were balanced using SMOTE. With a balanced working dataset, we constructed 3 random forest classifiers: a multi-class predictor, a Severe disease group binary classifier and a PASC binary classifier. Models were also analyzed for feature importance to identify relevant cytokines to generate a disease score. Multi-class models generated a score specific for the PASC patients and defined as S1 = (IFN-g + IL-2)/CCL4-MIP-1b. Second, a score for the Severe COVID-19 patients was defined as S2 = (IL-6+sCD40L/1000 + VEGF/10 + 10*IL-10)/(IL-2 + IL-8). Severe COVID-19 patients are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While PASC patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.
dc.description.procedenceVicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informática
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)
dc.description.procedenceVicerrectoría de Docencia::Salud::Facultad de Microbiología
dc.identifier.doihttps://doi.org/10.3389/fimmu.2021.700782
dc.identifier.issn1664-3224
dc.identifier.urihttps://hdl.handle.net/10669/102731
dc.language.isoeng
dc.rightsacceso restringido
dc.sourceFrontiers, 12, Artículo 700782
dc.subjectpost-acute sequelae of COVID-19
dc.subjectCOVID-19
dc.subjectcytokines
dc.subjectchemokines
dc.subjectmachine learning
dc.titleImmune-based prediction of COVID-19 severity and chronicity decoded using machine learning
dc.typeartículo original

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