Finite element (FE) modeling is becoming increasingly prevalent in the world of cardiac mechanics; however, many existing FE models are phenomenological and thus do not capture cellular-level mechanics. This work implements a cellular-level contraction scheme into an existing nonlinear FE code to model ventricular contraction. Specifically, this contraction model incorporates three myosin states: OFF-, ON-, and an attached force-generating state. It has been speculated that force-dependent transitions from the OFF- to ON-state may contribute to length-dependent activation at the cellular level. The current work investigates the contribution of force-dependent recruitment out of the OFF-state to ventricular-level function, specifically the Frank-Starling relationship, as seen through the end-systolic pressure-volume relationship (ESPVR). Five FE models were constructed using geometries of rat left ventricles obtained via cardiac magnetic resonance imaging. FE simulations were conducted to optimize parameters for the cellular contraction model such that the differences between FE predicted ventricular pressures for the models and experimentally measured pressures were minimized. The models were further validated by comparing FE predicted end-systolic strain to experimentally measured strain. Simulations mimicking vena cava occlusion generated descending pressure volume loops from which ESPVRs were calculated. In simulations with the inclusion of the OFF-state, using a force-dependent transition to the ON-state, the ESPVR calculated was steeper than in simulations excluding the OFF-state. Furthermore, the ESPVR was also steeper when compared to models that included the OFF-state without a force-dependent transition. This suggests that the force-dependent recruitment of thick filament heads from the OFF-state at the cellular level contributes to the Frank-Starling relationship observed at the organ level.

中文翻译：

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肌球蛋白关闭状态的力依赖性募集增加左心室收缩末期压力-容积关系。

有限元 (FE) 建模在心脏力学领域变得越来越流行；然而，许多现有的有限元模型是现象​​学的，因此不能捕捉细胞水平的力学。这项工作将细胞水平的收缩方案实施到现有的非线性有限元代码中，以模拟心室收缩。具体来说，这种收缩模型包含三种肌球蛋白状态：OFF-、ON-和附着力产生状态。据推测，从 OFF 状态到 ON 状态的依赖于力的转变可能有助于细胞水平上的长度依赖激活。当前的工作调查了从 OFF 状态到心室水平功能的依赖于力的募集的贡献，特别是 Frank-Starling 关系，如通过收缩末期压力-容积关系 (ESPVR) 所见。使用通过心脏磁共振成像获得的大鼠左心室的几何形状构建了五个 FE 模型。进行 FE 模拟以优化细胞收缩模型的参数，从而使模型的 FE 预测心室压力与实验测量压力之间的差异最小化。通过将 FE 预测的收缩末期应变与实验测量的应变进行比较，进一步验证了模型。模拟腔静脉闭塞的模拟产生了下降的压力容积环，从中可以计算出 ESPVR。在包含关闭状态的模拟中，使用依赖于力的转换到开启状态，计算出的 ESPVR 比不包括关闭状态的模拟更陡峭。此外，与包含 OFF 状态而没有依赖于力的转换的模型相比，ESPVR 也更陡峭。这表明在细胞水平从关闭状态对粗丝头的力依赖性募集有助于在器官水平观察到的 Frank-Starling 关系。