Autor según el artículo: Rodríguez-Tomàs, E; Iftimie, S; Castañé, H; Baiges-Gaya, G; Hernández-Aguilera, A; González-Viñas, M; Castro, A; Camps, J; Joven, J
Departamento: Medicina i Cirurgia
Autor/es de la URV: 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 Paraoxonase-1 Monocyte chemoattractant protein-1 Machine learning Galectin-3 Covid-19 Chemokines Biomarkers sars-cov-2 pathogens paraoxonase-1 oxidative stress mitochondria machine learning hdl galectin-3 fibrosis density-lipoprotein covid-19 chemokines cells
Resumen: SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed ‘cytokine storm’ that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2), and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2, and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation, and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19.
Áreas temáticas: Química Physiology Molecular biology Medicina ii Medicina i Interdisciplinar Food science & technology Food science Farmacia Engenharias ii Clinical biochemistry Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Chemistry, medicinal Cell biology Biotecnología Biodiversidade 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 helena.castane@estudiants.urv.cat gerard.baiges@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat jorge.joven@urv.cat antoni.castro@urv.cat
Identificador del autor: 0000-0003-0714-8414 0000-0002-3165-3640 0000-0003-2749-4541 0000-0001-5441-6333
Fecha de alta del registro: 2024-07-27
Volumen de revista: 10
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2076-3921/10/6/991
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Antioxidants. 10 (6): 991-
Referencia de l'ítem segons les normes APA: Rodríguez-Tomàs, E; Iftimie, S; Castañé, H; Baiges-Gaya, G; Hernández-Aguilera, A; González-Viñas, M; Castro, A; Camps, J; Joven, J (2021). Clinical performance of paraoxonase-1-related variables and novel markers of inflammation in coronavirus disease-19. A machine learning approach. Antioxidants, 10(6), 991-. DOI: 10.3390/antiox10060991
DOI del artículo: 10.3390/antiox10060991
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2021
Tipo de publicación: Journal Publications