压力超负荷心脏生长和重构的计算机模拟--将微观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
摘要：
心脏生长和重塑（G&R）是指心肌组织响应于负荷条件的慢性改变而发生的结构变化。一种这样的状况是压力超负荷，其中升高的壁应力刺激心肌细胞厚度的生长，与器官规模的向心性肥大的表型相关，并促进纤维化。最初的肥大反应可以被认为是适应性的，有利于肌细胞存活，但随着时间的推移，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适应不良机制开始占主导地位，最终介导向明显的心力衰竭表型的转变。肌细胞水平的生物学因素与全身和器官水平的生物力学因素之间的潜在机制仍然知之甚少。计算模型的G&R显示高承诺作为一个独特的框架，提供心肌应力和应变之间的定量联系，在器官规模的生物调节过程中，在细胞水平上的管理肥大反应。然而，微观结构的动机，严格验证的计算模型的G&R仍处于起步阶段。本文综述了目前研究心脏G&R的最新计算模型。讨论了心肌细胞内的微观结构和机械感应/机械转导，并总结了以前的实验和临床研究的定量数据。最后，我们讨论了未来研究的主要挑战和可能的方向，可以推进心脏G&R计算建模的当前状态。
英文摘要：
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.
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