论文标题
在压力超负荷心脏中心脏生长和重塑的计算建模 - 将微结构与器官表型联系起来
Computational Modeling of Cardiac Growth and Remodeling in Pressure Overloaded Hearts -- Linking Microstructure to Organ Phenotype
论文作者
论文摘要
心脏生长和重塑(G&R)是指响应加载条件的慢性改变,是心肌组织的结构变化。这样的条件是压力超负荷,其中升高的壁应力刺激心肌细胞厚度的生长,与器官尺度下同心肥大的表型相关,并促进纤维化。最初的肥大反应可以通过偏爱肌细胞的存活来视为适应性和有益,但是随着时间的流逝,如果压力超负荷条件持续存在,有利于细胞死亡和纤维化的不良调整机制开始占主导地位,最终介导了向明显的心力衰竭表型的过渡。在系统性和器官水平的生物力学因素与生物力学因素联系起来的基本机制仍然知之甚少。 G&R的计算模型表现出很高的希望,作为一个独特的框架,用于在器官尺度上与控制肥厚反应的细胞水平的生物调节过程之间的心肌应力和菌株之间的定量联系。但是,G&R的微观结构动机,严格验证的计算模型仍处于起步阶段。本文概述了研究心脏G&R的计算模型的当前最新图案。讨论了心肌细胞内的微观结构和机械传感/机械转导,并总结了先前的实验和临床研究的定量数据。最后,我们讨论了主要挑战和未来研究的可能方向,这些研究可以推动心脏G&R计算建模的当前状态。
Cardiac growth and remodeling (G&R) refers to structural changes in myocardial tissue in response to chronic alterations in loading conditions. One such condition is pressure overload where elevated wall stresses stimulate the growth in cardiomyocyte thickness, associated with a phenotype of concentric hypertrophy at the organ scale, and promote fibrosis. The initial hypertrophic response can be considered adaptive and beneficial by favoring myocyte survival, but over time if pressure overload conditions persist, maladaptive mechanisms favoring cell death and fibrosis start to dominate, ultimately mediating the transition towards an overt heart failure phenotype. The underlying mechanisms linking biological factors at the myocyte level to biomechanical factors at the systemic and organ level remain poorly understood. Computational models of G&R show high promise as a unique framework for providing a quantitative link between myocardial stresses and strains at the organ scale to biological regulatory processes at the cellular level which govern the hypertrophic response. However, microstructurally motivated, rigorously validated computational models of G&R are still in their infancy. This article provides an overview of the current state-of-the-art of computational models to study cardiac G&R. The microstructure and mechanosensing/mechanotransduction within cells of the myocardium is discussed and quantitative data from previous experimental and clinical studies is summarized. We conclude with a discussion of major challenges and possible directions of future research that can advance the current state of cardiac G&R computational modeling.