Articles producció científica> Medicina i Cirurgia

Clinical performance of paraoxonase-1-related variables and novel markers of inflammation in coronavirus disease-19. A machine learning approach

  • Dades identificatives

    Identificador: imarina:9219143
    Autors:
    Rodríguez-Tomàs, EIftimie, SCastañé, HBaiges-Gaya, GHernández-Aguilera, AGonzález-Viñas, MCastro, ACamps, JJoven, J
    Resum:
    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.
  • Altres:

    Autor segons l'article: Rodríguez-Tomàs, E; Iftimie, S; Castañé, H; Baiges-Gaya, G; Hernández-Aguilera, A; González-Viñas, M; Castro, A; Camps, J; Joven, J
    Departament: Medicina i Cirurgia
    Autor/s 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
    Paraules clau: 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
    Resum: 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'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 de l'autor: 0000-0003-0714-8414 0000-0002-3165-3640 0000-0003-2749-4541 0000-0001-5441-6333
    Data d'alta del registre: 2024-07-27
    Volum de revista: 10
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2076-3921/10/6/991
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Antioxidants. 10 (6): 991-
    Referència 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 de l'article: 10.3390/antiox10060991
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Biochemistry,Biochemistry & Molecular Biology,Cell Biology,Chemistry, Medicinal,Clinical Biochemistry,Food Science,Food Science & Technology,Molecular Biology,Physiology
    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
    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
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