Comparison of stress and stress–strain approaches for the active contraction in a rat cardiac cycle model

Martonová, Denisa; Holz, David; Seufert, Julia; Dương, Minh Tuấn; Alkassar, Muhannad; Leyendecker, Sigrid

doi:10.1016/j.jbiomech.2022.110980

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…For example, EF and EDP in the healthy model are 65.6% and 1 kPa (7.5 mmHg), respectively. These are close to the experimentally reported values in rats, which are slightly above the normal values in humans ( 35 , 42 , 43 ). By increasing the amount of fibrosis to 30% and 50%, EF reduces to 56.1% and 46.1%, whereas EDP increases to 1.6 kPa (12 mmHg) and 1.8 kPa (13.5 mmHg), respectively; refer to Figures 3B,E .…”

Section: Resultssupporting

confidence: 90%

“…In the present study, the total PK2 is additively decomposed into the passive part S pas and the active part S act ( 31 – 35 ), namely…”

Section: Methodsmentioning

confidence: 99%

“…The constitutive model is applied to the generic ellipsoidal rat LV based on the data from echocardiography. For more details regarding the geometry, fiber orientation, compressibility, and the Windkessel model serving as a boundary condition in Equation (1), we refer to previous study ( 35 ).…”

Section: Methodsmentioning

confidence: 99%

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Support Pressure Acting on the Epicardial Surface of a Rat Left Ventricle—A Computational Study

Martonová

Holz

Brackenhammer

et al. 2022

Front. Cardiovasc. Med.

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The present computational study investigates the effects of an epicardial support pressure mimicking a heart support system without direct blood contact. We chose restrictive cardiomyopathy as a model for a diseased heart. By changing one parameter representing the amount of fibrosis, this model allows us to investigate the impairment in a diseased left ventricle, both during diastole and systole. The aim of the study is to determine the temporal course and value of the support pressure that leads to a normalization of the cardiac parameters in diseased hearts. These are quantified via the end-diastolic pressure, end-diastolic volume, end-systolic volume, and ejection fraction. First, the amount of fibrosis is increased to model diseased hearts at different stages. Second, we determine the difference in the left ventricular pressure between a healthy and diseased heart during a cardiac cycle and apply for the epicardial support as the respective pressure difference. Third, an epicardial support pressure is applied in form of a piecewise constant step function. The support is provided only during diastole, only during systole, or during both phases. Finally, the support pressure is adjusted to reach the corresponding parameters in a healthy rat. Parameter normalization is not possible to achieve with solely diastolic or solely systolic support; for the modeled case with 50% fibrosis, the ejection fraction can be increased by 5% with purely diastolic support and 14% with purely systolic support. However, the ejection fraction reaches the value of the modeled healthy left ventricle (65.6%) using a combination of diastolic and systolic support. The end-diastolic pressure of 13.5 mmHg cannot be decreased with purely systolic support. However, the end-diastolic pressure reaches the value of the modeled healthy left ventricle (7.5 mmHg) with diastolic support as well as with the combination of the diastolic and systolic support. The resulting negative diastolic support pressure is −4.5 mmHg, and the positive systolic support pressure is 90 mmHg. We, thereby, conclude that ventricular support during both diastole and systole is beneficial for normalizing the left ventricular ejection fraction and the end-diastolic pressure, and thus it is a potentially interesting therapy for cardiac insufficiency.

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Section: Resultssupporting

confidence: 90%

“…In the present study, the total PK2 is additively decomposed into the passive part S pas and the active part S act ( 31 – 35 ), namely…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Support Pressure Acting on the Epicardial Surface of a Rat Left Ventricle—A Computational Study

Martonová

Holz

Brackenhammer

et al. 2022

Front. Cardiovasc. Med.

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show abstract

“…Today, most popular models for heart muscle tissue are Holzapfel-type models [32] that use an invariantbased formulation of the free energy function [62] and can naturally incorporate tissue incompressibility and orthotropy in the fiber, sheet, and normal directions [33]. Invariant-based modeling of cardiac tissue has rapidly gained popularity [16,23,24,30,45,46,52] and is now widely used in many common finite element packages [1,7]. Notably, the initial Holzapfel Ogden model was made up of four exponential quadratic terms in the first invariant I 1 , the fourth invariants I 4f and I 4s , and the eighth invariant I 8fs , with two parameters each; one with the unit of stiffness and the other unit-less [33].…”

Section: Motivationmentioning

confidence: 99%

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

Martonová,

Peirlinck,

Linka

et al. 2024

Preprint

Self Cite

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For more than half a century, scientists have developed mathematical models to understand the behavior of the human heart. Today, we have dozens of heart tissue models to choose from, but selecting the best model is limited to expert professionals, prone to user bias, and vulnerable to human error. Here we take the human out of the loop and automate the process of model discovery. Towards this goal, we establish a novel incompressible orthotropic constitutive neural network to simultaneously discover both, model and parameters, that best explain human cardiac tissue. Notably, our network features 32 individual terms, 8 isotropic and 24 anisotropic, and fully autonomously selects the best model, out of more than 4 billion possible combinations of terms. We demonstrate that we can successfully train the network with triaxial shear and biaxial extension tests and systematically sparsify the parameter vector withL1-regularization. Strikingly, we robustly discover a four-term model that features a quadratic term in the second invariantI2, and exponential quadratic terms in the fourth and eighth invariantsI4f,I4n, andI8fs. Importantly, our discovered model is interpretable by design and has parameters with well-defined physical units. We show that it outperforms popular existing myocardium models and generalizes well, from homogeneous laboratory tests to heterogeneous whole heart simulations. This is made possible by a new universal material subroutine that directly takes the discovered network weights as input. Automating the process of model discovery has the potential to democratize cardiac modeling, broaden participation in scientific discovery, and accelerate the development of innovative treatments for cardiovascular disease.Our source code, data, and examples are available athttps://github.com/LivingMatterLab/CANN.

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Automated model discovery for human cardiac tissue: Discovering the best model and parameters

Martonová,

Peirlinck,

Linka

et al. 2024

Computer Methods in Applied Mechanics and Engineering

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Comparison of stress and stress–strain approaches for the active contraction in a rat cardiac cycle model

Cited by 5 publications

References 40 publications

Support Pressure Acting on the Epicardial Surface of a Rat Left Ventricle—A Computational Study

Support Pressure Acting on the Epicardial Surface of a Rat Left Ventricle—A Computational Study

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

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