000109592 001__ 109592
000109592 005__ 20221004075847.0
000109592 0247_ $$2doi$$a10.1007/s10237-020-01415-3
000109592 0248_ $$2sideral$$a123309
000109592 037__ $$aART-2021-123309
000109592 041__ $$aeng
000109592 100__ $$0(orcid)0000-0001-7620-3355$$aEscuer, J.
000109592 245__ $$aInfluence of vessel curvature and plaque composition on drug transport in the arterial wall following drug-eluting stent implantation
000109592 260__ $$c2021
000109592 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109592 5203_ $$aIn the last decade, many computational models have been developed to describe the transport of drug eluted from stents and the subsequent uptake into arterial tissue. Each of these models has its own set of limitations: for example, models typically employ simplified stent and arterial geometries, some models assume a homogeneous arterial wall, and others neglect the influence of blood flow and plasma filtration on the drug transport process. In this study, we focus on two common limitations. Specifically, we provide a comprehensive investigation of the influence of arterial curvature and plaque composition on drug transport in the arterial wall following drug-eluting stent implantation. The arterial wall is considered as a three-layered structure including the subendothelial space, the media and the adventitia, with porous membranes separating them (endothelium, internal and external elastic lamina). Blood flow is modelled by the Navier–Stokes equations, while Darcy’s law is used to calculate plasma filtration through the porous layers. Our findings demonstrate that arterial curvature and plaque composition have important influences on the spatiotemporal distribution of drug, with potential implications in terms of effectiveness of the treatment. Since the majority of computational models tend to neglect these features, these models are likely to be under- or over-estimating drug uptake and redistribution in arterial tissue.
000109592 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T24-17R$$9info:eu-repo/grantAgreement/ES/DGA/LMP121-18$$9info:eu-repo/grantAgreement/ES/ISCIII/CIBER$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2014-069737$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-76630-C2-1-R
000109592 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000109592 590__ $$a3.623$$b2021
000109592 592__ $$a0.761$$b2021
000109592 594__ $$a5.3$$b2021
000109592 591__ $$aBIOPHYSICS$$b32 / 72 = 0.444$$c2021$$dQ2$$eT2
000109592 593__ $$aMechanical Engineering$$c2021$$dQ1
000109592 591__ $$aENGINEERING, BIOMEDICAL$$b55 / 98 = 0.561$$c2021$$dQ3$$eT2
000109592 593__ $$aBiotechnology$$c2021$$dQ1
000109592 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000109592 700__ $$aAznar, I.
000109592 700__ $$aMcCormick, C.
000109592 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, E.$$uUniversidad de Zaragoza
000109592 700__ $$aMcGinty, S.
000109592 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, M.A.$$uUniversidad de Zaragoza
000109592 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000109592 773__ $$g(2021), [20 pp]$$pBiomech. model. mechanobiol.$$tBIOMECHANICS AND MODELING IN MECHANOBIOLOGY$$x1617-7959
000109592 85641 $$uhttps://rdcu.be/cf8sk$$zTexto completo de la revista
000109592 8564_ $$s1904576$$uhttps://zaguan.unizar.es/record/109592/files/texto_completo.pdf$$yPostprint
000109592 8564_ $$s2458208$$uhttps://zaguan.unizar.es/record/109592/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000109592 909CO $$ooai:zaguan.unizar.es:109592$$particulos$$pdriver
000109592 951__ $$a2022-10-03-13:57:24
000109592 980__ $$aARTICLE