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血管生成动力学:结构适应性血管网络内血管内流动的计算模型

Angiogenesis Dynamics: A Computational Model of Intravascular Flow Within a Structural Adaptive Vascular Network.

作者信息

Nivlouei Sahar Jafari, Guerra Ana, Belinha Jorge, Mangir Naside, MacNeil Sheila, Salgado Christiane, Monteiro Fernando Jorge, Natal Jorge Renato

机构信息

INEGI-Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, Portugal.

ISEP-Instituto Superior de Engenharia do Porto, Departamento de Engenharia Mecânica, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal.

出版信息

Biomedicines. 2024 Dec 13;12(12):2845. doi: 10.3390/biomedicines12122845.

Abstract

BACKGROUND

Understanding vascular development and the key factors involved in regulating angiogenesis-the growth of new blood vessels from pre-existing vasculature-is crucial for developing therapeutic approaches to promote wound healing. Computational techniques offer valuable insights into improving angiogenic strategies, leading to enhanced tissue regeneration and improved outcomes for chronic wound healing. While chorioallantoic membrane (CAM) models are widely used for examining fundamental mechanisms in vascular development, they lack quantification of essential parameters such as blood flow rate, intravascular pressure, and changes in vessel diameter.

METHODS

To address this limitation, the current study develops a novel two-dimensional mathematical model of angiogenesis, integrating discrete and continuous modelling approaches to capture intricate cellular interactions and provide detailed information about the capillary network's structure. The proposed hybrid meshless-based model simulates sprouting angiogenesis using the in vivo CAM system.

RESULTS

The model successfully predicts the branching process with a total capillary volume fraction deviation of less than 15% compared to experimental data. Additionally, it implements blood flow through the capillary network and calculates the distribution of intravascular pressure and vessel wall shear stress. An adaptive network is introduced to consider capillary responses to hemodynamic and metabolic stimuli, reporting structural diameter changes across the generated vasculature network. The model demonstrates its robustness by verifying numerical outcomes, revealing statistically significant differences with deviations in key parameters, including diameter, wall shear stress ( < 0.05), circumferential wall stress, and metabolic stimuli ( < 0.01).

CONCLUSION

With its strong predictive capability in simulating intravascular flow and its ability to provide both quantitative and qualitative assessments, this research enhances our understanding of angiogenesis by introducing a biologically relevant network that addresses the functional demands of the tissue.

摘要

背景

了解血管发育以及调控血管生成(即从现有脉管系统生长出新血管)的关键因素,对于开发促进伤口愈合的治疗方法至关重要。计算技术为改进血管生成策略提供了有价值的见解,从而促进组织再生并改善慢性伤口愈合的效果。虽然绒毛尿囊膜(CAM)模型被广泛用于研究血管发育的基本机制,但它们缺乏对诸如血流速率、血管内压力和血管直径变化等关键参数的量化。

方法

为解决这一局限性,本研究开发了一种新型的二维血管生成数学模型,整合离散和连续建模方法以捕捉复杂的细胞相互作用,并提供有关毛细血管网络结构的详细信息。所提出的基于无网格的混合模型使用体内CAM系统模拟发芽血管生成。

结果

该模型成功预测了分支过程,与实验数据相比,总毛细血管体积分数偏差小于15%。此外,它实现了通过毛细血管网络的血流,并计算血管内压力和血管壁剪切应力的分布。引入了一个自适应网络来考虑毛细血管对血流动力学和代谢刺激的反应,报告生成的脉管系统网络中结构直径的变化。该模型通过验证数值结果证明了其稳健性,揭示了关键参数(包括直径、壁剪切应力(<0.05)、周向壁应力和代谢刺激(<0.01))偏差的统计学显著差异。

结论

该研究通过引入一个满足组织功能需求的生物学相关网络,增强了我们对血管生成的理解,该网络在模拟血管内血流方面具有强大的预测能力,并能够提供定量和定性评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bedc/11673541/c6c1431c3023/biomedicines-12-02845-g001.jpg

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