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Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

2022-07Journal article Open access
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dc.contributor.authorCobre, Alexandre de Fátima
dc.contributor.authorSurek, Monica
dc.contributor.authorStremel, Dile Pontarolo
dc.contributor.authorFachi, Mariana Millan
dc.contributor.authorBorba, Helena Hiemisch
dc.contributor.authorTonin, Fernanda
dc.contributor.authorPontarolo, Roberto
dc.date.accessioned2022-06-07T10:32:27Z
dc.date.available2024-06-07T00:30:24Z
dc.date.issued2022-07
dc.description.abstractObjective: To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes. Material and methods: Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe, and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG, and KNN. Results: The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity, and fatality of COVID-19 of 93%, 94%, and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine, and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19. Conclusion: The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity, and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients’ serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCobre AF, Surek M, Stremel DP, Fachi MM, Borba HH, Tonin FS, et al. Diagnosis and prognosis of COVID-19 employing analysis of patients’ plasma and serum via LC-MS and machine learning. Comput Biol Med. 2022;146:105659.pt_PT
dc.identifier.doi10.1016/j.compbiomed.2022.105659pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/14694
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0010482522004516pt_PT
dc.subjectCOVID-19pt_PT
dc.subjectFatalitypt_PT
dc.subjectSeveritypt_PT
dc.subjectDiagnosispt_PT
dc.subjectBiomarkerpt_PT
dc.subjectMachine learningpt_PT
dc.titleDiagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage105659pt_PT
oaire.citation.titleComputers in Biology and Medicinept_PT
oaire.citation.volume146pt_PT
person.familyNameTonin
person.givenNameFernanda
person.identifier.ciencia-idD01C-C700-9411
person.identifier.orcid0000-0003-4262-8608
person.identifier.ridO-2050-2017
person.identifier.scopus-author-id56085115800
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication61ded30e-ecec-4b3e-b953-2293e080ebdd
relation.isAuthorOfPublication.latestForDiscovery61ded30e-ecec-4b3e-b953-2293e080ebdd

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