000153672 001__ 153672
000153672 005__ 20251017144611.0
000153672 0247_ $$2doi$$a10.3389/fbioe.2025.1549104
000153672 0248_ $$2sideral$$a143755
000153672 037__ $$aART-2025-143755
000153672 041__ $$aeng
000153672 100__ $$aCaballero, Ricardo$$uUniversidad de Zaragoza
000153672 245__ $$aFully coupled hybrid in-silico modeling of atherosclerosis: A multi-scale framework integrating CFD, transport phenomena and agent-based modeling
000153672 260__ $$c2025
000153672 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153672 5203_ $$aIntroduction: Atherosclerosis is a complex disease influenced by biological and mechanical factors, leading to plaque formation within arterial walls. Understanding the interplay between hemodynamics, cellular interactions, and biochemical transport is crucial for predicting disease progression and evaluating therapeutic strategies.
Methods: We developed a hybrid in-silico model integrating computational fluid dynamics (CFD), mass transport, and agent-based modeling to simulate plaque progression in coronary arteries. The model incorporates key factors such as wall shear stress (WSS), low-density lipoprotein (LDL) filtration, and the interaction between smooth muscle cells (SMCs), cytokines, and extracellular matrix (ECM).
Results: Our simulations demonstrate that the integration of CFD, transport phenomena, and agent-based modeling provides a comprehensive framework for predicting atherosclerotic plaque growth. The model successfully captures the mechanobiological interactions driving plaque development and suggests potential mechanisms underlying lesion progression.
Discussion: The proposed methodology establishes a foundation for developing computational platforms to test therapeutic interventions, such as anti-inflammatory drugs and lipid-lowering agents, under patient-specific conditions. These findings highlight the potential of hybrid multi-scale in-silico models to advance the understanding of atherosclerosis and support the development of personalized treatment strategies.
000153672 536__ $$9nfo:eu-repo/grantAgreement/ES/AEI/PID2022-140219OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN PRE2020-095671
000153672 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000153672 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153672 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, Miguel Ángel$$uUniversidad de Zaragoza
000153672 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, Estefanía$$uUniversidad de Zaragoza
000153672 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000153672 773__ $$g13 (2025), 1549104 [22 pp.]$$pFront. Bioeng. Biotechnol.$$tFrontiers in Bioengineering and Biotechnology$$x2296-4185
000153672 8564_ $$s5152256$$uhttps://zaguan.unizar.es/record/153672/files/texto_completo.pdf$$yVersión publicada
000153672 8564_ $$s1780465$$uhttps://zaguan.unizar.es/record/153672/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153672 909CO $$ooai:zaguan.unizar.es:153672$$particulos$$pdriver
000153672 951__ $$a2025-10-17-14:17:25
000153672 980__ $$aARTICLE