An investigation of the glycosaminoglycan contribution to biaxial mechanical behaviours of porcine atrioventricular heart valve leaflets

Multiscale structure and function of the aortic valve apparatus

Whereas studying the aortic valve in isolation has facilitated the development of life-saving procedures and technologies, the dynamic interplay of the aortic valve and its surrounding structures is vital to preserving their function across the wide range of conditions encountered in an active lifestyle. Our view is that these structures should be viewed as an integrated functional unit, here referred to as the aortic valve apparatus (AVA). The coupling of the aortic valve and root, left ventricular outflow tract, and blood circulation is crucial for AVA's functions: unidirectional flow out of the left ventricle, coronary perfusion, reservoir function, and support of left ventricular function. In this review, we explore the multiscale biological and physical phenomena that underlie the simultaneous fulfillment of these functions. A brief overview of the tools used to investigate the AVA, such as medical imaging modalities, experimental methods, and computational modeling, specifically fluid-structure interaction (FSI) simulations, is included. Some pathologies affecting the AVA are explored, and insights are provided on treatments and interventions that aim to maintain quality of life. The concepts explained in this article support the idea of AVA being an integrated functional unit and help identify unanswered research questions. Incorporating phenomena through the molecular, micro, meso, and whole tissue scales is crucial for understanding the sophisticated normal functions and diseases of the AVA.

MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics

Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification. Recently, PDE-solving deep learning methods, such as neural operators, are starting to make an important impact on learning and predicting the response of a complex physical system directly from observational data. Taking the material modeling problems for example, the neural operator approach learns a surrogate mapping from the loading field to the corresponding material response field, which can be seen as learning the solution operator of a hidden PDE. The microstructure and mechanical parameters of each material specimen correspond to the (possibly heterogeneous) parameter field in this hidden PDE. Due to the limitation on experimental measurement techniques, the data acquisition for each material specimen is commonly challenging and costly. This fact calls for the utilization and transfer of existing knowledge to new and unseen material specimens, which corresponds to sampling efficient learning of the solution operator of a hidden PDE with a different parameter field. Herein, we propose a novel meta-learning approach for neural operators, which can be seen as transferring the knowledge of solution operators between governing (unknown) PDEs with varying parameter fields. Our approach is a provably universal solution operator for multiple PDE solving tasks, with a key theoretical observation that underlying parameter fields can be captured in the first layer of neural operator models, in contrast to typical final-layer transfer in existing meta-learning methods. As applications, we demonstrate the efficacy of our proposed approach on PDE-based datasets and a real-world material modeling problem, illustrating that our method can handle complex and nonlinear physical response learning tasks while greatly improving the sampling efficiency in unseen tasks.

An investigation of how specimen dimensions affect biaxial mechanical characterizations with CellScale BioTester and constitutive modeling of porcine tricuspid valve leaflets

Biaxial mechanical characterizations are the accepted approach to determine the mechanical response of many biological soft tissues. Although several computational and experimental studies have examined how experimental factors (e.g., clamped vs. suture mounting) affect the acquired tissue mechanical behavior, little is known about the role of specimen dimensions in data acquisition and the subsequent modeling. In this study, we combined our established mechanical characterization framework with an iterative size-reduction protocol to test the hypothesis that specimen dimensions affect the observed mechanical behavior of biaxial characterizations. Our findings indicated that there were non-significant differences in the peak equibiaxial stretches of tricuspid valve leaflets across four specimen dimensions ranging from 4.5×4.5mm to 9 × 9mm. Further analyses revealed that there were significant differences in the low-tensile modulus of the circumferential tissue direction. These differences resulted in significantly different constitutive model parameters for the Tong-Fung model between different specimen dimensions of the posterior and septal leaflets. Overall, our findings demonstrate that specimen dimensions play an important role in experimental characterizations, but not necessarily in constitutive modeling of soft tissue mechanical behavior during biaxial testing with the commercial CellScale BioTester.

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.

Anisotropicity and flexibility in trilayered microfibrous substrates promote heart valve leaflet tissue engineering

Heart valve leaflet substrates with native trilayer and anisotropic structures are crucial for successful heart valve tissue engineering. In this study, we used the electrospinning technique to produce trilayer microfibrous leaflet substrates using two biocompatible and biodegradable polymers-poly (L-lactic acid) (PLLA) and polycaprolactone (PCL), separately. Different polymer concentrations for each layer were applied to bring a high degree of mechanical and structural anisotropy to the substrates. PCL leaflet substrates exhibited lower unidirectional tensile properties than PLLA leaflet substrates. However, the PLLA substrates exhibited a lower flexural modulus than the PCL substrates. These substrates were seeded with porcine valvular interstitial cells (PVICs) and cultured for one month in static conditions. Both substrates exhibited cellular adhesion and proliferation, resulting in the production of tissue-engineered constructs. The PLLA tissue-engineered constructs had more cellular growth than the PCL tissue-engineered constructs. The PLLA substrates showed higher hydrophilicity, lower crystallinity, and more significant anisotropy than PCL substrates, which may have enhanced their interactions with PVICs. Analysis of gene expression showed higherα-smooth muscle actin and collagen type 1 expression in PLLA tissue-engineered constructs than in PCL tissue-engineered constructs. The differences in anisotropic and flexural properties may have accounted for the different cellular behaviors in these two individual polymer substrates.

Effects of enzyme-based removal of collagen and elastin constituents on the biaxial mechanical responses of porcine atrioventricular heart valve anterior leaflets

The leaflets of the atrioventricular heart valves (AHVs) regulate the one-directional flow of blood through a coordination of the extracellular matrix components, including the collagen fibers, elastin, and glycosaminoglycans. Dysfunction of the AHVs, such as those caused by unfavorable microstructural remodeling, lead to valvular heart diseases and improper blood flow, which can ultimately cause heart failure. In order to better understand the mechanics and remodeling of the AHV leaflets and how therapeutics can inadvertently cause adverse microstructural changes, a systematic characterization of the role of each constituent in the biomechanical properties is appropriate. Previous studies have quantified the contributions of the individual microstructural components to tissue-level behavior for the semilunar valve cusps, but not for the AHV leaflets. In this study, for the first time, we quantify the relationships between microstructure and mechanics of the AHV leaflet using a three-step experimental procedure: (i) biaxial tension and stress relaxation testing of control (untreated) porcine AHV anterior leaflet specimens; (ii) enzyme treatment to remove a portion of either the collagen or elastin constituent; and (iii) biaxial tensile and stress relaxation testing of the constituent-removed (treated) specimens. We have observed that the removal of ∼100% elastin resulted in a ∼10% decrease in the tissue extensibility with biaxial tension and a ∼10% increase in the overall stress reduction with stress relaxation. In contrast, removal of 46% of the collagen content insignificantly affected tissue extensibility with biaxial tension and significantly increased stress decay (10%) with stress relaxation. These findings provide an insight into the microstructure-mechanics relationship of the AHVs and will be beneficial for future developments and refinements of microstructurally informed constitutive models for the simulation of diseased and surgically intervened AHV function. STATEMENT OF SIGNIFICANCE: This study presents, for the first time, a thorough mechanical characterization of the atrioventricular heart valve leaflets before and after enzymatic removal of elastin and collagen. We found that the biaxial tensile properties of elastin-deficient tissues and collagen-deficient are stiffer. The fact of elastin supporting low-stress valve function and collagen as the main load-bearing component was evident in a decrease in the low-tension modulus for elastin-deficient tissues and in the high-tension modulus for collagen-deficient tissues. Our quantification and experimental technique could be useful in predicting the disease-related changes in heart valve mechanics. The information obtained from this work is valuable for refining the constitutive models that describe the essential microstructure-mechanics relationship.

Parameterization, geometric modeling, and isogeometric analysis of tricuspid valves

Approximately 1.6 million patients in the United States are affected by tricuspid valve regurgitation, which occurs when the tricuspid valve does not close properly to prevent backward blood flow into the right atrium. Despite its critical role in proper cardiac function, the tricuspid valve has received limited research attention compared to the mitral and aortic valves on the left side of the heart. As a result, proper valvular function and the pathologies that may cause dysfunction remain poorly understood. To promote further investigations of the biomechanical behavior and response of the tricuspid valve, this work establishes a parameter-based approach that provides a template for tricuspid valve modeling and simulation. The proposed tricuspid valve parameterization presents a comprehensive description of the leaflets and the complex chordae tendineae for capturing the typical three-cusp structural deformation observed from medical data. This simulation framework develops a practical procedure for modeling tricuspid valves and offers a robust, flexible approach to analyze the performance and effectiveness of various valve configurations using isogeometric analysis. The proposed methods also establish a baseline to examine the tricuspid valve's structural deformation, perform future investigations of native valve configurations under healthy and disease conditions, and optimize prosthetic valve designs.

A pilot in silico modeling-based study of the pathological effects on the biomechanical function of tricuspid valves

Current clinical assessment of functional tricuspid valve regurgitation relies on metrics quantified from medical imaging modalities. Although these clinical methodologies are generally successful, the lack of detailed information about the mechanical environment of the valve presents inherent challenges for assessing tricuspid valve regurgitation. In the present study, we have developed a finite element-based in silico model of one porcine tricuspid valve (TV) geometry to investigate how various pathological conditions affect the overall biomechanical function of the TV. There were three primary observations from our results. Firstly, the results of the papillary muscle (PM) displacement study scenario indicated more pronounced changes in the TV biomechanical function. Secondly, compared to uniform annulus dilation, nonuniform dilation scenario induced more evident changes in the von Mises stresses (83.8-125.3 kPa vs 65.1-84.0 kPa) and the Green-Lagrange strains (0.52-0.58 vs 0.47-0.53) for the three TV leaflets. Finally, results from the pulmonary hypertension study scenario showed opposite trends compared to the PM displacement and annulus dilation scenarios. Furthermore, various chordae rupture scenarios were simulated, and the results showed that the chordae tendineae attached to the TV anterior and septal leaflets may be more critical to proper TV function. This in silico modeling-based study has provided a deeper insight into the tricuspid valve pathologies that may be useful, with moderate extensions, for guiding clinical decisions. NOVELTY STATEMENT: The novelties of the research are summarized below: A comprehensive in silico pilot study of how isolated functional tricuspid regurgitation pathologies and ruptured chordae tendineae would alter the tricuspid valve function; An extensive analysis of the tricuspid valve function, including mechanical quantities (eg, the von Mises stress and the Green-Lagrange strain) and clinically-relevant geometry metrics (eg, the tenting area and the coaptation height); and A developed computational modeling pipeline that can be extended to evaluate patient-specific tricuspid valve geometries and enhance the current clinical diagnosis and treatment of tricuspid regurgitation.

Mechanics and Microstructure of the Atrioventricular Heart Valve Chordae Tendineae: A Review

The atrioventricular heart valves (AHVs) are responsible for directing unidirectional blood flow through the heart by properly opening and closing the valve leaflets, which are supported in their function by the chordae tendineae and the papillary muscles. Specifically, the chordae tendineae are critical to distributing forces during systolic closure from the leaflets to the papillary muscles, preventing leaflet prolapse and consequent regurgitation. Current therapies for chordae failure have issues of disease recurrence or suboptimal treatment outcomes. To improve those therapies, researchers have sought to better understand the mechanics and microstructure of the chordae tendineae of the AHVs. The intricate structures of the chordae tendineae have become of increasing interest in recent literature, and there are several key findings that have not been comprehensively summarized in one review. Therefore, in this review paper, we will provide a summary of the current state of biomechanical and microstructural characterizations of the chordae tendineae, and also discuss perspectives for future studies that will aid in a better understanding of the tissue mechanics-microstructure linking of the AHVs' chordae tendineae, and thereby improve the therapeutics for heart valve diseases caused by chordae failures.

An investigation of the effect of freezing storage on the biaxial mechanical properties of excised porcine tricuspid valve anterior leaflets

The atrioventricular heart valve (AHV) leaflets are critical to the facilitation of proper unidirectional blood flow through the heart. Previously, studies have been conducted to understand the tissue mechanics of healthy AHV leaflets to inform the development of valve-specific computational models and replacement materials for use in diagnosing and treating valvular heart disease. Generally, these studies involved biaxial mechanical testing of the AHV leaflet tissue specimens to extract relevant mechanical properties. Most of those studies considered freezing-based storage systems based on previous findings for other connective tissues such as aortic tissue or skin. However, there remains no study that specifically examines the effects of freezing storage on the characterized mechanical properties of the AHV leaflets. In this study, we aimed to address this gap in knowledge by performing biaxial mechanical characterizations of the tricuspid valve anterior leaflet (TVAL) tissue both before and after a 48-h freezing period. Primary findings of this study include: (i) a statistically insignificant change in the tissue extensibilities, with the frozen tissues being slightly stiffer and more anisotropic than the fresh tissues; and (ii) minimal variations in the stress relaxation behaviors between the fresh and frozen tissues, with the frozen tissues demonstrating slightly lessened relaxation. The findings from this study suggested that freezing-based storage does not significantly impact the observed mechanical properties of one of the five AHV leaflets-the TVAL. The results from this study are useful for reaffirming the experimental methodologies in the previous studies, as well as informing the tissue preservation methods of future investigations of AHV leaflet mechanics.

An investigation of the glycosaminoglycan contribution to biaxial mechanical behaviours of porcine atrioventricular heart valve leaflets

The atrioventricular heart valve (AHV) leaflets have a complex microstructure composed of four distinct layers: atrialis, ventricularis, fibrosa and spongiosa. Specifically, the spongiosa layer is primarily proteoglycans and glycosaminoglycans (GAGs). Quantification of the GAGs' mechanical contribution to the overall leaflet function has been of recent focus for aortic valve leaflets, but this characterization has not been reported for the AHV leaflets. This study seeks to expand current GAG literature through novel mechanical characterizations of GAGs in AHV leaflets. For this characterization, mitral and tricuspid valve anterior leaflets (MVAL and TVAL, respectively) were: (i) tested by biaxial mechanical loading at varying loading ratios and by stress-relaxation procedures, (ii) enzymatically treated for removal of the GAGs and (iii) biaxially mechanically tested again under the same protocols as in step (i). Removal of the GAG contents from the leaflet was conducted using a 100 min enzyme treatment to achieve approximate 74.87% and 61.24% reductions of all GAGs from the MVAL and TVAL, respectively. Our main findings demonstrated that biaxial mechanical testing yielded a statistically significant difference in tissue extensibility after GAG removal and that stress-relaxation testing revealed a statistically significant smaller stress decay of the enzyme-treated tissue than untreated tissues. These novel findings illustrate the importance of GAGs in AHV leaflet behaviour, which can be employed to better inform heart valve therapeutics and computational models.