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静脉移植物新生内膜增生的患者特异性多尺度建模

Patient-Specific, Multi-Scale Modeling of Neointimal Hyperplasia in Vein Grafts.

作者信息

Donadoni Francesca, Pichardo-Almarza Cesar, Bartlett Matthew, Dardik Alan, Homer-Vanniasinkam Shervanthi, Díaz-Zuccarini Vanessa

机构信息

Mechanical Engineering, University College LondonLondon, UK.

The Department of Surgery, Yale University School of MedicineNew Haven, CT, USA.

出版信息

Front Physiol. 2017 Apr 18;8:226. doi: 10.3389/fphys.2017.00226. eCollection 2017.

Abstract

Neointimal hyperplasia is amongst the major causes of failure of bypass grafts. The disease progression varies from patient to patient due to a range of different factors. In this paper, a mathematical model will be used to understand neointimal hyperplasia in individual patients, combining information from biological experiments and patient-specific data to analyze some aspects of the disease, particularly with regard to mechanical stimuli due to shear stresses on the vessel wall. By combining a biochemical model of cell growth and a patient-specific computational fluid dynamics analysis of blood flow in the lumen, remodeling of the blood vessel is studied by means of a novel computational framework. The framework was used to analyze two vein graft bypasses from one patient: a femoro-popliteal and a femoro-distal bypass. The remodeling of the vessel wall and analysis of the flow for each case was then compared to clinical data and discussed as a potential tool for a better understanding of the disease. Simulation results from this first computational approach showed an overall agreement on the locations of hyperplasia in these patients and demonstrated the potential of using new integrative modeling tools to understand disease progression.

摘要

新生内膜增生是旁路移植失败的主要原因之一。由于一系列不同因素,疾病进展在患者之间存在差异。在本文中,将使用数学模型来理解个体患者的新生内膜增生,结合生物学实验信息和患者特定数据来分析该疾病的某些方面,特别是关于血管壁上剪切应力引起的机械刺激。通过结合细胞生长的生化模型和管腔内血流的患者特定计算流体动力学分析,借助一种新颖的计算框架研究血管重塑。该框架用于分析一名患者的两条静脉移植旁路:股腘旁路和股远端旁路。然后将每种情况的血管壁重塑和血流分析与临床数据进行比较,并作为更好理解该疾病的潜在工具进行讨论。这种第一种计算方法的模拟结果在这些患者增生位置上显示出总体一致性,并证明了使用新的综合建模工具来理解疾病进展的潜力。

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