| Literature DB >> 34369107 |
Martin L Tomov1, Lilanni Perez2, Liqun Ning1, Huang Chen1, Bowen Jing1, Andrew Mingee1, Sahar Ibrahim1, Andrea S Theus1, Gabriella Kabboul1, Katherine Do2, Sai Raviteja Bhamidipati3, Jordan Fischbach1, Kevin McCoy1, Byron A Zambrano3, Jianyi Zhang4, Reza Avazmohammadi3,5, Athanasios Mantalaris1, Brooks D Lindsey1,6, David Frakes1,6, Lakshmi Prasad Dasi1, Vahid Serpooshan1,2,7, Holly Bauser-Heaton1,2,7,8.
Abstract
Vascular atresia are often treated via transcatheter recanalization or surgical vascular anastomosis due to congenital malformations or coronary occlusions. The cellular response to vascular anastomosis or recanalization is, however, largely unknown and current techniques rely on restoration rather than optimization of flow into the atretic arteries. An improved understanding of cellular response post anastomosis may result in reduced restenosis. Here, an in vitro platform is used to model anastomosis in pulmonary arteries (PAs) and for procedural planning to reduce vascular restenosis. Bifurcated PAs are bioprinted within 3D hydrogel constructs to simulate a reestablished intervascular connection. The PA models are seeded with human endothelial cells and perfused at physiological flow rate to form endothelium. Particle image velocimetry and computational fluid dynamics modeling show close agreement in quantifying flow velocity and wall shear stress within the bioprinted arteries. These data are used to identify regions with greatest levels of shear stress alterations, prone to stenosis. Vascular geometry and flow hemodynamics significantly affect endothelial cell viability, proliferation, alignment, microcapillary formation, and metabolic bioprofiles. These integrated in vitro-in silico methods establish a unique platform to study complex cardiovascular diseases and can lead to direct clinical improvements in surgical planning for diseases of disturbed flow.Entities:
Keywords: 3D bioprinting; anastomosis; bifurcated vessels; particle image velocimetry; pulmonary artery atresia
Mesh:
Year: 2021 PMID: 34369107 PMCID: PMC8823098 DOI: 10.1002/adhm.202100968
Source DB: PubMed Journal: Adv Healthc Mater ISSN: 2192-2640 Impact factor: 11.092