Autor según el artículo: Baiges-Gaya, G; Iftimie, S; Castañé, H; Rodríguez-Tomàs, E; Jiménez-Franco, A; López-Azcona, AF; Castro, A; Camps, J; Joven, J
Departamento: Medicina i Cirurgia
Autor/es de la URV: Alkhoury, Nadine / Baiges Gaya, Gerard / Camps Andreu, Jorge / Castañé Vilafranca, Helena / Castro Salomó, Antoni / Iftimie Iftimie, Simona Mihaela / Joven Maried, Jorge / Rodriguez Tomas, Elisabet
Palabras clave: Sars-cov-2 Metabolomics Machine learning Enveloped viruses Covid-19 Biomarkers xylitol survival sars-cov-2 multi-omics metabolomics machine learning inactivation disease diagnosis covid-19
Resumen: Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.
Áreas temáticas: Química Molecular biology Materiais General medicine Farmacia Ensino Biochemistry & molecular biology Biochemistry
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: simonamihaela.iftime@urv.cat jorge.camps@urv.cat antoni.castro@urv.cat jorge.joven@urv.cat elisabet.rodriguezt@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat gerard.baiges@estudiants.urv.cat helena.castane@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat
Identificador del autor: 0000-0003-0714-8414 0000-0002-3165-3640 0000-0001-5441-6333 0000-0003-2749-4541
Fecha de alta del registro: 2023-12-17
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2218-273X/13/1/163
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Biomolecules. 13 (1): 163-
Referencia de l'ítem segons les normes APA: Baiges-Gaya, G; Iftimie, S; Castañé, H; Rodríguez-Tomàs, E; Jiménez-Franco, A; López-Azcona, AF; Castro, A; Camps, J; Joven, J (2023). Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19. Biomolecules, 13(1), 163-. DOI: 10.3390/biom13010163
DOI del artículo: 10.3390/biom13010163
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications
Repositori URV