Carbon nanotubes’ effect on mitochondrial oxygen flux dynamics: polarography experimental study and machine learning models using star graph trace invariants of Raman spectra

UDC.coleccionInvestigaciónes_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.grupoInvRedes de Neuronas Artificiais e Sistemas Adaptativos -Informática Médica e Diagnóstico Radiolóxico (RNASA - IMEDIR)es_ES
UDC.grupoInvRNASA - IMEDIR (INIBIC)es_ES
UDC.institutoCentroINIBIC - Instituto de Investigacións Biomédicas de A Coruñaes_ES
UDC.issue11es_ES
UDC.journalTitleNanomaterialses_ES
UDC.startPage386es_ES
UDC.volume7es_ES
dc.contributor.authorGonzález-Durruthy, Michael
dc.contributor.authorMonserrat, José M.
dc.contributor.authorRasulev, Bakhtiyor
dc.contributor.authorCasañola-Martín, Gerardo M.
dc.contributor.authorBarreiro Sorrivas, José María
dc.contributor.authorParaíso-Medina, Sergio
dc.contributor.authorMaojo, Víctor
dc.contributor.authorGonzález-Díaz, Humberto
dc.contributor.authorPazos, A.
dc.contributor.authorMunteanu, Cristian-Robert
dc.date.accessioned2017-12-15T09:59:51Z
dc.date.available2017-12-15T09:59:51Z
dc.date.issued2017-11-11
dc.description.abstract[Abstract] This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; PI13/02020es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; PI13/00280es_ES
dc.description.sponsorshipGalicia. Consellería de Educación, Cultura e Educación Universitaria; R2014/025es_ES
dc.description.sponsorshipGalicia. Consellería de Educación, Cultura e Educación Universitaria; GRC2014/049es_ES
dc.description.sponsorshipGalicia. Consellería de Educación, Cultura e Educación Universitaria; R2014/039es_ES
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividad; UNLC08-1E-002es_ES
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividad; UNLC13-13-3503es_ES
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividad; CTQ2016-74881-Pes_ES
dc.description.sponsorshipPaís Vasco.Gobierno; IT1045-16es_ES
dc.identifier.citationGonzález-Durruthy M, Monserrat JM, Rasulev B, et al. Carbon nanotubes’ effect on mitochondrial oxygen flux dynamics: polarography experimental study and machine learning models using star graph trace invariants of Raman spectra. Nanomaterials (Basel). 2017;7(11):386es_ES
dc.identifier.issn2079-4991
dc.identifier.urihttp://hdl.handle.net/2183/19916
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttp://dx.doi.org/10.3390/nano7110386es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCarbon nanotubeses_ES
dc.subjectCytotoxicityes_ES
dc.subjectMitochondria oxygen mass fluxes_ES
dc.subjectRaman spectroscopyes_ES
dc.subjectGraph theoryes_ES
dc.subjectSpectral momentses_ES
dc.titleCarbon nanotubes’ effect on mitochondrial oxygen flux dynamics: polarography experimental study and machine learning models using star graph trace invariants of Raman spectraes_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationfa192a4c-bffd-4b23-87ae-e68c29350cdc
relation.isAuthorOfPublicationfac98c9d-7cc7-4b09-bbb1-1068637fc73f
relation.isAuthorOfPublication.latestForDiscoveryfa192a4c-bffd-4b23-87ae-e68c29350cdc

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