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Cytokine-based insights into bloodstream infections and bacterial gram typing in ICU COVID-19 patients

2025-03-16Research article Open access
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authorProfile.emailbiblioteca@isel.pt
datacite.subject.fosEngenharia e Tecnologia::Engenharia Química
dc.contributor.authorAraújo, Rúben Alexandre Dinis
dc.contributor.authorRamalhete, Luís
dc.contributor.authorVon Rekowski, Cristiana
dc.contributor.authorHenrique Fonseca, Tiago Alexandre
dc.contributor.authorCalado, Cecília
dc.contributor.authorBento, Luís
dc.date.accessioned2025-04-02T08:13:54Z
dc.date.available2025-04-02T08:13:54Z
dc.date.issued2025-03-16
dc.description.abstractTimely and accurate identification of bloodstream infections (BSIs) in intensive care unit (ICU) patients remains a key challenge, particularly in COVID-19 settings, where immune dysregulation can obscure early clinical signs. Methods: Cytokine profiling was evaluated to discriminate between ICU patients with and without BSIs, and, among those with confirmed BSIs, to further stratify bacterial infections by Gram type. Serum samples from 45 ICU COVID-19 patients were analyzed using a 21-cytokine panel, with feature selection applied to identify candidate markers. Results: A machine learning workflow identified key features, achieving robust performance metrics with AUC values up to 0.97 for BSI classification and 0.98 for Gram typing. Conclusions: In contrast to traditional approaches that focus on individual cytokines or simple ratios, the present analysis employed programmatically generated ratios between pro-inflammatory and anti-inflammatory cytokines, refined through feature selection. Although further validation in larger and more diverse cohorts is warranted, these findings underscore the potential of advanced cytokine-based diagnostics to enhance precision medicine in infection management.eng
dc.description.sponsorshipPL/IDI&CA2024/R-DICIP_ISEL - Instituto Politécnico de Lisboa (IPL)
dc.identifier.citationAraújo, R., Ramalhete, L., Von Rekowski, C. P., Fonseca, T. A. H., Calado, C. R. C., & Bento, L. (2025). Cytokine-based insights into bloodstream infections and bacterial gram typing in ICU COVID-19 patients. Metabolites, 15(3), 204. https://doi.org/10.3390/metabo15030204
dc.identifier.doihttps://doi.org/10.3390/metabo15030204
dc.identifier.eissn2218-1989
dc.identifier.urihttp://hdl.handle.net/10400.21/21729
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationDSAIPA/DS/0117/2020 - Fundação para a Ciência e a Tecnologia (FCT)
dc.relation.hasversionhttps://www.mdpi.com/2218-1989/15/3/204
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCytokine profiling
dc.subjectBloodstream infections
dc.subjectGram typing
dc.subjectICU diagnostics
dc.subjectCOVID-19
dc.subjectMachine learning
dc.titleCytokine-based insights into bloodstream infections and bacterial gram typing in ICU COVID-19 patientseng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage25
oaire.citation.issue3
oaire.citation.startPage1
oaire.citation.titleMetabolites
oaire.citation.volume15
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAraújo
person.familyNameVon Rekowski
person.familyNameHenrique Fonseca
person.familyNameCalado
person.givenNameRúben Alexandre Dinis
person.givenNameCristiana
person.givenNameTiago Alexandre
person.givenNameCecília
person.identifier1960990
person.identifier130332
person.identifier.ciencia-id9A18-BFDC-ED95
person.identifier.ciencia-id8F1D-1D48-8551
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0002-9369-6486
person.identifier.orcid0009-0009-6843-1935
person.identifier.orcid0000-0003-0741-2211
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id6603163260
relation.isAuthorOfPublication9998e940-5e65-4661-8308-afcb56d5df01
relation.isAuthorOfPublicationaa62d0cd-948b-45a0-9717-459b247dae86
relation.isAuthorOfPublicationd4a391dd-4551-44df-a326-c17e796b0945
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublication.latestForDiscovery9998e940-5e65-4661-8308-afcb56d5df01

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