Chan Jason K D, Chadwick Eric A, Taniguchi Daisuke, Ahmadipour Mohammadali, Suzuki Takaya, Romero David, Amon Cristina, Waddell Thomas K, Karoubi Golnaz, Bazylak Aimy
Department of Mechanical and Industrial Engineering, 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.
Front Bioeng Biotechnol. 2022 Apr 27;10:891407. doi: 10.3389/fbioe.2022.891407. eCollection 2022.
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.
我们创建了一个瞬态计算流体动力学模型，该模型具有一个包含惯性的颗粒沉积概率函数，用于量化小鼠肺血管系统中细胞的运输和沉积，以实现无细胞器官的再内皮化。当基于常见临床实践进行实验验证时，我们的新型惯性算法与两种既定的颗粒沉积算法相比，细胞接种效率误差降低了73%。我们通过将注射流速从3.81毫升/分钟提高到9.40毫升/分钟，提高了肺血管系统中细胞分布的均匀性。结果，数值结果和实验结果中的细胞接种效率分别提高了42%和66%。
