Repositorio Institucional de Documentos

000153131 001__ 153131
000153131 005__ 20251017144633.0
000153131 0247_ $$2doi$$a10.1080/25310429.2025.2477911
000153131 0248_ $$2sideral$$a143601
000153131 037__ $$aART-2025-143601
000153131 041__ $$aeng
000153131 100__ $$aEsteban, Patricia
000153131 245__ $$aCombination of exhaled volatile organic compounds with serum biomarkers predicts respiratory infection severity
000153131 260__ $$c2025
000153131 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153131 5203_ $$aObjective. During respiratory infections, host-pathogen interaction alters metabolism, leading to changes in the composition of expired volatile organic compounds (VOCs) and soluble immunomodulators. This study aims to identify VOC and blood biomarker signatures to develop machine learning-based prognostic models capable of distinguishing infections with similar symptoms.
Methods. Twenty-one VOCs and fifteen serum biomarkers were quantified in samples from 86 COVID-19 patients, 75 patients with non-COVID-19 respiratory infections, and 72 healthy donors. The populations were categorized into severity subgroups based on their oxygen support requirements. Descriptive and statistical analyses were conducted to assess group differentiation. Additionally, machine learning classifiers were developed to predict disease severity in both COVID-19 and non-COVID-19 patients.
Results. VOC and biomarker profiles differed significantly among groups. Random Forest models demonstrated the best performance for severity prediction. The COVID-19 model achieved 93% accuracy, 100% sensitivity, and 89% specificity, identifying IL-6, IL-8, thrombomodulin, and toluene as key severity predictors. In non-COVID-19 patients, the model reached 89% accuracy, 100% sensitivity, and 67% specificity, with CXCL10 and methyl-isobutyl-ketone as key markers.
Conclusion.VOCs and serum biomarkers differentiated HD, COVID-19, and non-COVID-19 patients, and enabled the development of high-performance severity prediction models. While promising, these findings require validation in larger independent cohorts.
000153131 536__ $$9info:eu-repo/grantAgreement/ES/IACS/PT20-00112
000153131 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000153131 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153131 700__ $$aLetona-Gimenez, Santiago
000153131 700__ $$aDomingo, Maria Pilar
000153131 700__ $$0(orcid)0000-0001-9262-2461$$aMorte, Elena$$uUniversidad de Zaragoza
000153131 700__ $$aPellejero-Sagastizabal, Galadriel$$uUniversidad de Zaragoza
000153131 700__ $$0(orcid)0000-0003-4891-105X$$aEncabo, Maria del Mar
000153131 700__ $$0(orcid)0000-0002-3888-7036$$aRamírez-Labrada, Ariel$$uUniversidad de Zaragoza
000153131 700__ $$aSanz-Pamplona, Rebeca
000153131 700__ $$0(orcid)0000-0003-0154-0730$$aPardo, Julián$$uUniversidad de Zaragoza
000153131 700__ $$0(orcid)0000-0002-9600-8116$$aPaño, José Ramón$$uUniversidad de Zaragoza
000153131 700__ $$0(orcid)0000-0001-6928-5516$$aGalvez, Eva M.
000153131 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000153131 7102_ $$11011$$2566$$aUniversidad de Zaragoza$$bDpto. Microb.Ped.Radio.Sal.Pú.$$cÁrea Inmunología
000153131 7102_ $$11002$$2050$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Biología Celular
000153131 773__ $$g31, 1 (2025), [11 pp.]$$pPulmonolgy$$tPulmonolgy$$x2531-0429
000153131 8564_ $$s3654443$$uhttps://zaguan.unizar.es/record/153131/files/texto_completo.pdf$$yVersión publicada
000153131 8564_ $$s1234446$$uhttps://zaguan.unizar.es/record/153131/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153131 909CO $$ooai:zaguan.unizar.es:153131$$particulos$$pdriver
000153131 951__ $$a2025-10-17-14:27:22
000153131 980__ $$aARTICLE

El sistema ha encontrado un error mientras gestionaba su petición.

Los administradores del sistema han sido avisados.

En caso de duda, póngase en contacto con deposita@unizar.es