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

Frontiers in Bioengineering and Biotechnology

Golnaz Karoubi¹  David Romero²  Aimy Bazylak²  Jason K.D. Chan²  Eric A. Chadwick²  Takaya Suzuki³  Mohammadali Ahmadipour⁴  Cristina Amon⁴  Thomas K. Waddell⁵  Daisuke Taniguchi⁶ 

[1] Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada;Department of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan;Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada;Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada;Latner Thoracic Surgery Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada;

关键词: lung tissue engineering;    lung regeneration;    computational fluid dynamics;    cell seeding;    re-endothelialization;    inertia;   

DOI  :  10.3389/fbioe.2022.891407

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown