Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization

We created a transient computational fluid dynamics model featuring a particle deposition probability function that incorporates inertia to quantify the transport and deposition of cells in mouse lung vasculature for the re-endothelialization of the acellular organ. Our novel inertial algorithm demo...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in bioengineering and biotechnology 2022-04, Vol.10, p.891407-891407
Main Authors: Chan, Jason K D, Chadwick, Eric A, Taniguchi, Daisuke, Ahmadipour, Mohammadali, Suzuki, Takaya, Romero, David, Amon, Cristina, Waddell, Thomas K, Karoubi, Golnaz, Bazylak, Aimy
Format: Article
Language:English
Subjects:
Bioengineering and Biotechnology
cell seeding
computational fluid dynamics
inertia
lung regeneration
lung tissue engineering
re-endothelialization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We created a transient computational fluid dynamics model featuring a particle deposition probability function that incorporates inertia to quantify the transport and deposition of cells in mouse lung vasculature for the re-endothelialization of the acellular organ. Our novel inertial algorithm demonstrated a 73% reduction in cell seeding efficiency error compared to two established particle deposition algorithms when validated with experiments based on common clinical practices. We enhanced the uniformity of cell distributions in the lung vasculature by increasing the injection flow rate from 3.81 ml/min to 9.40 ml/min. As a result, the cell seeding efficiency increased in both the numerical and experimental results by 42 and 66%, respectively.
ISSN:2296-4185
2296-4185
DOI:10.3389/fbioe.2022.891407