A novel fibre-ensemble level constitutive model for exogenous cross-linked collagenous tissues

Effect of aging on the biaxial mechanical behavior of human descending thoracic aorta: Experiments and constitutive modeling considering collagen crosslinking

Collagen crosslinking, an important contributor to the stiffness of soft tissues, was found to increase with aging in the aortic wall. Here we investigated the mechanical properties of human descending thoracic aorta with aging and the role of collagen crosslinking through a combined experimental and modeling approach. A total of 32 samples from 17 donors were collected and divided into three age groups: <40, 40-60 and > 60 years. Planar biaxial tensile tests were performed to characterize the anisotropic mechanical behavior of the aortic samples. A recently developed constitutive model incorporating collagen crosslinking into the two-fiber family model (Holzapfel and Ogden, 2020) was modified to accommodate biaxial deformation of the aorta, in which the extension and rotation kinematics of bonded fibers and crosslinks were decoupled. The mechanical testing results show that the aorta stiffens with aging with a more drastic change in the longitudinal direction, which results in altered aortic anisotropy. Our results demonstrate a good fitting capability of the constitutive model considering crosslinking for the biaxial aortic mechanics of all age groups. Furthermore, constitutive modeling results suggest an increased contribution of crosslinking and strain energy density to the biaxial stress-stretch behaviors with aging and point to excessive crosslinking as a prominent contributor to aortic stiffening.

Simulation of Mitral Valve Plasticity in Response to Myocardial Infarction

Left ventricular myocardial infarction (MI) has broad and debilitating effects on cardiac function. In many cases, MI leads to ischemic mitral regurgitation (IMR), a condition characterized by incompetency of the mitral valve (MV). IMR has many deleterious effects as well as a high mortality rate. While various clinical treatments for IMR exist, success of these procedures remains limited, in large part because IMR dramatically alters the geometry and function of the MV in ways that are currently not well understood. Previous investigations of post-MI MV remodeling have elucidated that MV tissues have a significant ability to undergo a form of permanent inelastic deformations in the first phase of the post-MI period. These changes appear to be attributable to the altered loading and boundary conditions on the MV itself, as opposed to an independent pathophysiological process. Mechanistically, these results suggest that the MV mostly responds passively to MI during the first 8 weeks post-MI by undergoing a permanent deformation. In the present study, we developed the first computational model of this post-MI MV remodeling process, which we term "mitral valve plasticity." Integrating methodologies and insights from previous studies of in vivo ovine MV function, image-based patient-specific model development, and post-MI MV adaptation, we constructed a representative geometric model of a pre-MI MV. We then performed finite element simulations of the entire MV apparatus under time-dependent boundary conditions and accounting for changes to material properties equivalent to those observed 0-8 weeks post-MI. Our results suggest that during this initial period of adaptation, the MV response to MI can be accurately modeled using a soft tissue plasticity approach, similar to permanent set frameworks that have been applied previously in the context of exogenously crosslinked tissues.

Neural Network Approaches for Soft Biological Tissue and Organ Simulations

Given the functional complexities of soft tissues and organs, it is clear that computational simulations are critical in their understanding and for the rational basis for the development of therapies and replacements. A key aspect of such simulations is accounting for their complex, nonlinear, anisotropic mechanical behaviors. While soft tissue material models have developed to the point of high fidelity, in-silico implementation is typically done using the finite element (FE) method, which remains impractically slow for translational clinical time frames. As a potential path toward addressing the development of high fidelity simulations capable of performing in clinically relevant time frames, we review the use of neural networks (NN) for soft tissue and organ simulation using two approaches. In the first approach, we show how a NN can learn the responses for a detailed meso-structural soft tissue material model. The NN material model not only reproduced the full anisotropic mechanical responses but also demonstrated a considerable efficiency improvement, as it was trained over a range of realizable fibrous structures. In the second approach, we go a step further with the use of a physics-based surrogate model to directly learn the displacement field solution without the need for raw training data or FE simulation datasets. In this approach we utilize a finite element mesh to define the domain and perform the necessary integrations, but not the finite element method (FEM) itself. We demonstrate with this approach, termed neural network finite element (NNFE), results in a trained NNFE model with excellent agreement with the corresponding "ground truth" FE solutions over the entire physiological deformation range on a cuboidal myocardium specimen. More importantly, the NNFE approach provided a significantly decreased computational time for a range of finite element mesh sizes. Specifically, as the FE mesh size increased from 2744 to 175,615 elements, the NNFE computational time increased from 0.1108 s to 0.1393 s, while the "ground truth" FE model increased from 4.541 s to 719.9 s, with the same effective accuracy. These results suggest that NNFE run times are significantly reduced compared with the traditional large-deformation-based finite element solution methods. We then show how a nonuniform rational B-splines (NURBS)-based approach can be directly integrated into the NNFE approach as a means to handle real organ geometries. While these and related approaches are in their early stages, they offer a method to perform complex organ-level simulations in clinically relevant time frames without compromising accuracy.

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.

Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics

Background

Medication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management.

Methods

This cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey. Demographic and clinical characteristics were extracted from the questionnaire and electronic medical records. The sample was randomly divided into a training dataset and a test dataset with a radio of 8:2 after data preprocessing. Four imputing methods, five sampling methods, three screening methods, and 18 machine learning algorithms were used to groom data and develop and validate models. Bootstrapping was performed to generate the validation set for external validation and univariate analysis. Models were compared on the basis of predictive performance metrics. Finally, we validated the sample size on the best model.

Results

This study included 980 patients with T2D, of whom 184 (18.8%) were defined as medication non-adherence. The results indicated that the model used modified random forest as the imputation method, random under sampler as the sampling method, Boruta as the feature screening method and the ensemble algorithms and had the best performance. The area under the receiver operating characteristic curve (AUC), F1 score, and area under the precision-recall curve (AUPRC) of the best model, among a total of 1,080 trained models, were 0.8369, 0.7912, and 0.9574, respectively. Age, present fasting blood glucose (FBG) values, present HbA1c values, present random blood glucose (RBG) values, and body mass index (BMI) were the most significant contributors associated with risks of medication adherence.

Conclusion

We found that machine learning methods could be used to predict the risk of non-adherence in patients with T2D. The proposed model was well performed to identify patients with T2D with non-adherence and could help improve individualized T2D management.

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid-structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

Anisotropic elastic behavior of a hydrogel-coated electrospun polyurethane: Suitability for heart valve leaflets

Although xenograft biomaterials have been used for decades in replacement heart valves, they continue to face multiple limitations, including limited durability, mineralization, and restricted design space due to their biological origins. These issues necessitate the need for novel replacement heart valve biomaterials that are durable, non-thrombogenic, and compatible with transcatheter aortic valve replacement devices. In this study, we explored the suitability of an electrospun poly(carbonate urethane) (ES-PCU) mesh coated with a poly(ethylene glycol) diacrylate (PEGDA) hydrogel as a synthetic biomaterial for replacement heart valve leaflets. In this material design, the mesh provides the mechanical support, while the hydrogel provides the required surface hemocompatibility. We conducted a comprehensive study to characterize the structural and mechanical properties of the uncoated mesh as well as the hydrogel-coated mesh (composite biomaterial) over the estimated operational range. We found that the composite biomaterial was functionally robust with reproducible stress-strain behavior within and beyond the functional ranges for replacement heart valves, and was able to withstand the rigors of mechanical evaluation without any observable damage. In addition, the composite biomaterial displayed a wide range of mechanical anisotropic responses, which were governed by fiber orientation of the mesh, which in turn, was controlled with the fabrication process. Finally, we developed a novel constitutive modeling approach to predict the mechanical behavior of the composite biomaterial under in-plane extension and shear deformation modes. This model identified the existence of fiber-fiber mechanical interactions in the mesh that have not previously been reported. Interestingly, there was no evidence of fiber-hydrogel mechanical interactions. This important finding suggests that the hydrogel coating can be optimized for hemocompatibility independent of the structural mechanical responses required by the leaflet. This initial study indicated that the composite biomaterial has mechanical properties well-suited for replacement heart valve applications and that the electrospun mesh microarchitecture and hydrogel biological properties can be optimized independently. It also reveals that the structural mechanisms contributing to the mechanical response are more complicated than what was previously established and paves the pathway for more detailed future studies.

Simulating the time evolving geometry, mechanical properties, and fibrous structure of bioprosthetic heart valve leaflets under cyclic loading

Currently, the most common replacement heart valve design is the 'bioprosthetic' heart valve (BHV), which has important advantages in that it does not require permanent anti-coagulation therapy, operates noiselessly, and has blood flow characteristics similar to the native valve. BHVs are typically fabricated from glutaraldehyde-crosslinked pericardial xenograft tissue biomaterials (XTBs) attached to a rigid, semi-flexible, or fully collapsible stent in the case of the increasingly popular transcutaneous aortic valve replacement (TAVR). While current TAVR assessments are positive, clinical results to date are generally limited to <2 years. Since TAVR leaflets are constructed using thinner XTBs, their mechanical demands are substantially greater than surgical BHV due to the increased stresses during in vivo operation, potentially resulting in decreased durability. Given the functional complexity of heart valve operation, in-silico predictive simulations clearly have potential to greatly improve the TAVR development process. As such simulations must start with accurate material models, we have developed a novel time-evolving constitutive model for pericardial xenograft tissue biomaterials (XTB) utilized in BHV (doi: 10.1016/j.jmbbm.2017.07.013). This model was able to simulate the observed tissue plasticity effects that occur in approximately in the first two years of in vivo function (50 million cycles). In the present work, we implemented this model into a complete simulation pipeline to predict the BHV time evolving geometry to 50 million cycles. The pipeline was implemented within an isogeometric finite element formulation that directly integrated our established BHV NURBS-based geometry (doi: 10.1007/s00466-015-1166-x). Simulations of successive loading cycles indicated continual changes in leaflet shape, as indicated by spatially varying increases in leaflet curvature. While the simulation model assumed an initial uniform fiber orientation distribution, anisotropic regional changes in leaflet tissue plastic strain induced a complex changes in regional fiber orientation. We have previously noted in our time-evolving constitutive model that the increases in collagen fiber recruitment with cyclic loading placed an upper bound on plastic strain levels. This effect was manifested by restricting further changes in leaflet geometry past 50 million cycles. Such phenomena was accurately captured in the valve-level simulations due to the use of a tissue-level structural-based modeling approach. Changes in basic leaflet dimensions agreed well with extant experimental studies. As a whole, the results of the present study indicate the complexity of BHV responses to cyclic loading, including changes in leaflet shape and internal fibrous structure. It should be noted that the later effect also influences changes in local mechanical behavior (i.e. changes in leaflet anisotropic tissue stress-strain relationship) due to internal fibrous structure resulting from plastic strains. Such mechanism-based simulations can help pave the way towards the application of sophisticated simulation technologies in the development of replacement heart valve technology.

Fully coupled dynamic simulations of bioprosthetic aortic valves based on an embedded strategy for fluid-structure interaction with contact

AIMS

This work aims at presenting a fully coupled approach for the numerical solution of contact problems between multiple elastic structures immersed in a fluid flow. The key features of the computational model are (i) a fully coupled fluid-structure interaction with contact, (ii) the use of a fibre-reinforced material for the leaflets, (iii) a stent, and (iv) a compliant aortic root.

METHODS AND RESULTS

The computational model takes inspiration from the immersed boundary techniques and allows the numerical simulation of the blood-tissue interaction of bioprosthetic heart valves (BHVs) as well as the contact among the leaflets. First, we present pure mechanical simulations, where blood is neglected, to assess the performance of different material properties and valve designs. Secondly, fully coupled fluid-structure interaction simulations are employed to analyse the combination of haemodynamic and mechanical characteristics. The isotropic leaflet tissue experiences high-stress values compared to the fibre-reinforced material model. Moreover, elongated leaflets show a stress concentration close to the base of the stent. We observe a fully developed flow at the systolic stage of the heartbeat. On the other hand, flow recirculation appears along the aortic wall during diastole.

CONCLUSION

The presented FSI approach can be used for analysing the mechanical and haemodynamic performance of a BHV. Our study suggests that stresses concentrate in the regions where leaflets are attached to the stent and in the portion of the aortic root where the BHV is placed. The results from this study may inspire new BHV designs that can provide a better stress distribution.

Isogeometric finite element-based simulation of the aortic heart valve: Integration of neural network structural material model and structural tensor fiber architecture representations

The functional complexity of native and replacement aortic heart valves (AVs) is well known, incorporating such physical phenomenons as time-varying non-linear anisotropic soft tissue mechanical behavior, geometric non-linearity, complex multi-surface time varying contact, and fluid-structure interactions to name a few. It is thus clear that computational simulations are critical in understanding AV function and for the rational basis for design of their replacements. However, such approaches continued to be limited by ad-hoc approaches for incorporating tissue fibrous structure, high-fidelity material models, and valve geometry. To this end, we developed an integrated tri-leaflet valve pipeline built upon an isogeometric analysis framework. A high-order structural tensor (HOST)-based method was developed for efficient storage and mapping the two-dimensional fiber structural data onto the valvular 3D geometry. We then developed a neural network (NN) material model that learned the responses of a detailed meso-structural model for exogenously cross-linked planar soft tissues. The NN material model not only reproduced the full anisotropic mechanical responses but also demonstrated a considerable efficiency improvement, as it was trained over a range of realizable fibrous structures. Results of parametric simulations were then performed, as well as population-based bicuspid AV fiber structure, that demonstrated the efficiency and robustness of the present approach. In summary, the present approach that integrates HOST and NN material model provides an efficient computational analysis framework with increased physical and functional realism for the simulation of native and replacement tri-leaflet heart valves.

Effect of macro-calcification on the failure mechanics of intracranial aneurysmal wall tissue

BACKGROUND

Calcification was recently found to be present in the majority of cerebral aneurysms, though how calcification and the presence or absence of co-localized lipid pools affect failure properties is still unknown.

OBJECTIVE

The primary objective is to quantify the biomechanical effect of a macro-calcification with surrounding Near-Calcification Region (NCR) of varying mechanical properties on tissue failure behavior.

METHODS

We utilized a structurally informed finite element model to simulate pre-failure and failure behavior of a human cerebral tissue specimen modeled as a composite containing a macro-calcification and surrounding NCR, embedded in a fiber matrix composite. Data from multiple imaging modalities was combined to quantify the collagen organization and calcification geometry. An idealized parametric model utilizing the calibrated model was used to explore the impact of NCR properties on tissue failure.

RESULTS

Compared to tissue without calcification, peak stress was reduced by 82% and 49% for low modulus (representing lipid pool) and high modulus (simulating increase in calcification size) of the NCR, respectively. Failure process strongly depended on NCR properties with lipid pools blunting the onset of complete failure. When the NCR was calcified, the sample was able to sustain larger overall stress, however the failure process was abrupt with nearly simultaneous failure of the loaded fibers.

CONCLUSIONS

Failure of calcified vascular tissue is strongly influenced by the ultrastructure in the vicinity of the calcification. Computational modeling of failure in fibrous soft tissues can be used to understand how pathological changes impact the tissue failure process, with potentially important clinical implications.

On collagen fiber morphoelasticity and homeostatic remodeling tone

A variety of biochemical and physical processes participate in the creation and maintenance of collagen in biological tissue. Under mechanical stimuli these collagen fibers undergo continuous processes of morphoelastic change. The model presented here is motivated by experimental reports of stretch-stabilization of the collagen fibers to enzymatic degradation. The fiber structure is modeled in terms of a fiber density evolution that is regulated by means of a fixed creation rate and a mechano-sensitive dissolution rate. The theory accounts for the possibly different natural configurations of the fiber unit constituents and the ground substance matrix. It also generalizes previous theoretical descriptions so as to account for finite survival times of the individual fiber units. Special consideration is given to steady state fiber-remodeling processes in which fiber creation and dissolution are in balance. Fiber assembly processes that involve prestretching the fiber constituents yield a homeostatic stress response with a characteristic fiber tone. Fiber density returns to homeostasis after mechanical disruption when sufficient time has passed.

Thinner biological tissues induce leaflet flutter in aortic heart valve replacements

Valvular heart disease has recently become an increasing public health concern due to the high prevalence of valve degeneration in aging populations. For patients with severely impacted aortic valves that require replacement, catheter-based bioprosthetic valve deployment offers a minimally invasive treatment option that eliminates many of the risks associated with surgical valve replacement. Although recent percutaneous device advancements have incorporated thinner, more flexible biological tissues to streamline safer deployment through catheters, the impact of such tissues in the complex, mechanically demanding, and highly dynamic valvular system remains poorly understood. The present work utilized a validated computational fluid-structure interaction approach to isolate the behavior of thinner, more compliant aortic valve tissues in a physiologically realistic system. This computational study identified and quantified significant leaflet flutter induced by the use of thinner tissues that initiated blood flow disturbances and oscillatory leaflet strains. The aortic flow and valvular dynamics associated with these thinner valvular tissues have not been previously identified and provide essential information that can significantly advance fundamental knowledge about the cardiac system and support future medical device innovation. Considering the risks associated with such observed flutter phenomena, including blood damage and accelerated leaflet deterioration, this study demonstrates the potentially serious impact of introducing thinner, more flexible tissues into the cardiac system.

Mitral valve leaflet response to ischaemic mitral regurgitation: from gene expression to tissue remodelling

Ischaemic mitral regurgitation (IMR), a frequent complication following myocardial infarction (MI), leads to higher mortality and poor clinical prognosis if untreated. Accumulating evidence suggests that mitral valve (MV) leaflets actively remodel post MI, and this remodelling increases both the severity of IMR and the occurrence of MV repair failures. However, the mechanisms of extracellular matrix maintenance and modulation by MV interstitial cells (MVICs) and their impact on MV leaflet tissue integrity and repair failure remain largely unknown. Herein, we sought to elucidate the multiscale behaviour of IMR-induced MV remodelling using an established ovine model. Leaflet tissue at eight weeks post MI exhibited significant permanent plastic radial deformation, eliminating mechanical anisotropy, accompanied by altered leaflet composition. Interestingly, no changes in effective collagen fibre modulus were observed, with MVICs slightly rounder, at eight weeks post MI. RNA sequencing indicated that YAP-induced genes were elevated at four weeks post MI, indicating elevated mechanotransduction. Genes related to extracellular matrix organization were downregulated at four weeks post MI when IMR occurred. Transcriptomic changes returned to baseline by eight weeks post MI. This multiscale study suggests that IMR induces plastic deformation of the MV with no functional damage to the collagen fibres, providing crucial information for computational simulations of the MV in IMR.

Direct and inverse identification of constitutive parameters from the structure of soft tissues. Part 2: dispersed arrangement of collagen fibers

This paper investigates on the relationship between the arrangement of collagen fibers within soft tissues and parameters of constitutive models. Starting from numerical experiments based on biaxial loading conditions, the study addresses both the direct (from structure to mechanics) and the inverse (from mechanics to structure) problems, solved introducing optimization problems for model calibration and regression analysis. A campaign of parametric analyses is conducted in order to consider fibers distributions with different main orientation and angular dispersion. Different anisotropic constitutive models are employed, accounting for fibers dispersion either with a generalized structural approach or with an increasing number of strain energy terms. Benchmark data sets, toward which constitutive models are fitted, are built by employing a multiscale description of fiber nonlinearities and accounting for fibers dispersion with an angular integration method. Results show how the optimal values of constitutive parameters obtained from model calibration vary as a function of fibers arrangement and testing protocol. Moreover, the fitting capabilities of constitutive models are discussed. A novel strategy for model calibration is also proposed, in order to obtain a robust accuracy with respect to different loading conditions starting from a low number of mechanical tests. Furthermore, novel results useful for the inverse determination of the mean angle and the variance of fibers distribution are obtained. Therefore, the study contributes: to better design procedures for model calibration; to account for mechanical alterations due to remodeling mechanisms; and to gain structural information in a nondestructive way.

Evaluation of transcatheter heart valve biomaterials: Computational modeling using bovine and porcine pericardium

OBJECTIVE

The durability of bioprosthetic heart valve (BHV) devices, commonly made of bovine (BP) and porcine (PP) pericardium tissue, is partly limited by device calcification and tissue degeneration, which has been associated with pathological levels of mechanical stress. This study investigated the impacts of BP and PP tissues with different thicknesses and tissue mechanical properties in BHV applications.

METHODS

Second Harmonic Generation (SHG) imaging was employed to visualize the collagen fibers on each side of the pericardium. Structural constitutive modeling that incorporates collagen fiber distribution obtained from multiphoton microscopy for each tissue type were derived to characterize the corresponding biaxial mechanical testing data collected in a previous study. The models were verified through finite element (FE) simulations of the biaxial test and implemented in valve closing simulations.

RESULTS

Smooth side collagen fibers were found to correlate with the mechanical response. BHVs with adult (ABP) and calf (CBP) BP tissues had lower maximum principal stresses than those with PP and fetal (FBP) BP tissues. Collagen fiber orientation along the circumferential axis resulted in lower maximum principal stresses and more uniform and symmetric stress distributions throughout the valve.

CONCLUSIONS

The use of PP and FBP tissue resulted in higher peak stresses than ABP and CBP tissues in the given valve design. Additionally, ensuring collagen fiber orientation along the circumferential axis led to lower maximum stresses felt by the valve leaflets, which could also improve BHV durability.

On the AIC-based model reduction for the general Holzapfel-Ogden myocardial constitutive law

Constitutive laws that describe the mechanical responses of cardiac tissue under loading hold the key to accurately model the biomechanical behaviour of the heart. There have been ample choices of phenomenological constitutive laws derived from experiments, some of which are quite sophisticated and include effects of microscopic fibre structures of the myocardium. A typical example is the strain-invariant-based Holzapfel–Ogden 2009 model that is excellently fitted to simple shear tests. It has been widely used and regarded as the state-of-the-art constitutive law for myocardium. However, there has been no analysis to show if it has both adequate descriptive and predictive capabilities for other tissue tests of myocardium. Indeed, such an analysis is important for any constitutive laws for clinically useful computational simulations. In this work, we perform such an analysis using combinations of tissue tests, uniaxial tension, biaxial tension and simple shear from three different sets of myocardial tissue studies. Starting from the general 14-parameter myocardial constitutive law developed by Holzapfel and Ogden, denoted as the general HO model, we show that this model has good descriptive and predictive capabilities for all the experimental tests. However, to reliably determine all 14 parameters of the model from experiments remains a great challenge. Our aim is to reduce the constitutive law using Akaike information criterion, to maintain its mechanical integrity whilst achieving minimal computational cost. A competent constitutive law should have descriptive and predictive capabilities for different tissue tests. By competent, we mean the model has least terms but is still able to describe and predict experimental data. We also investigate the optimal combinations of tissue tests for a given constitutive model. For example, our results show that using one of the reduced HO models, one may need just one shear response (along normal-fibre direction) and one biaxial stretch (ratio of 1 mean fibre : 1 cross-fibre) to satisfactorily describe Sommer et al. human myocardial mechanical properties. Our study suggests that single-state tests (i.e. simple shear or stretching only) are insufficient to determine the myocardium responses. We also found it is important to consider transmural fibre rotations within each myocardial sample of tests during the fitting process. This is done by excluding un-stretched fibres using an “effective fibre ratio”, which depends on the sample size, shape, local myofibre architecture and loading conditions. We conclude that a competent myocardium material model can be obtained from the general HO model using AIC analysis and a suitable combination of tissue tests.

Collagen fibre orientation and dispersion govern ultimate tensile strength, stiffness and the fatigue performance of bovine pericardium

The durability of bovine pericardium leaflets employed in bioprosthetic heart valves (BHVs) can significantly limit the longevity of heart valve prostheses. Collagen fibres are the dominant load bearing component of bovine pericardium, however fibre architecture within leaflet geometries is not explicitly controlled in the manufacture of commercial devices. Thus, the purpose of this study was to ascertain the influence of pre-determined collagen fibre orientation and dispersion on the mechanical performance of bovine pericardium. Three tissue groups were tested in uniaxial tension: cross-fibre tissue (XD); highly dispersed fibre-orientations (HD); or preferred-fibre tissue (PD). Both the XD and PD tissue were tested under cyclic loading at 1.5 Hz and a stress range of 2.7 MPa. The results of the static tensile experiments illustrated that collagen fibre orientation and degree of alignment significantly influenced the material's response, whereby, there was a statistically significant decrease in material properties between the XD groups and both the PD and HD groups for ultimate tensile strength and stiffness (p < 0.01). Furthermore, HD tissue had a stiffness of approximately 58% of the PD group, and XD tissue had a stiffness of approximately 18% of the PD group. The dynamic behaviour of the XD and PD groups was extremely distinct; for example a Weibull analysis indicated that the 50% probability of failure in specimens with fibres orientated perpendicular (XD) to the loading direction occurred at 375 cycles. Due to this failure, XD specimens survived on average less than 20% of the cycles completed by those in which fibres were aligned along the loading direction (PD). The results from this study indicate that fibre architecture is a significant factor in determining static strength and fatigue life in bovine pericardium, and thus must be incorporated in the design process to improve future device durability.

A Non-Invasive Material Characterization Framework for Bioprosthetic Heart Valves

Computational modeling and simulation has become more common in design and development of bioprosthetic heart valves. To have a reliable computational model, considering accurate mechanical properties of biological soft tissue is one of the most important steps. The goal of this study was to present a non-invasive material characterization framework to determine mechanical propertied of soft tissue employed in bioprosthetic heart valves. Using integrated experimental methods (i.e., digital image correlation measurements and hemodynamic testing in a pulse duplicator system) and numerical methods (i.e., finite element modeling and optimization), three-dimensional anisotropic mechanical properties of leaflets used in two commercially available transcatheter aortic valves (i.e., Edwards SAPIEN 3 and Medtronic CoreValve) were characterized and compared to that of a commonly used and well-examined surgical bioprosthesis (i.e., Carpentier-Edwards PERIMOUNT Magna aortic heart valve). The results of the simulations showed that the highest stress value during one cardiac cycle was at the peak of systole in the three bioprostheses. In addition, in the diastole, the peak of maximum in-plane principal stress was 0.98, 0.96, and 2.95 MPa for the PERIMOUNT Magna, CoreValve, and SAPIEN 3, respectively. Considering leaflet stress distributions, there might be a difference in the long-term durability of different TAV models.

An anisotropic constitutive model for immersogeometric fluid-structure interaction analysis of bioprosthetic heart valves

This paper considers an anisotropic hyperelastic soft tissue model, originally proposed for native valve tissue and referred to herein as the Lee-Sacks model, in an isogeometric thin shell analysis framework that can be readily combined with immersogeometric fluid-structure interaction (FSI) analysis for high-fidelity simulations of bioprosthetic heart valves (BHVs) interacting with blood flow. We find that the Lee-Sacks model is well-suited to reproduce the anisotropic stress-strain behavior of the cross-linked bovine pericardial tissues that are commonly used in BHVs. An automated procedure for parameter selection leads to an instance of the Lee-Sacks model that matches biaxial stress-strain data from the literature more closely, over a wider range of strains, than other soft tissue models. The relative simplicity of the Lee-Sacks model is attractive for computationally-demanding applications such as FSI analysis and we use the model to demonstrate how the presence and direction of material anisotropy affect the FSI dynamics of BHV leaflets.

Computational methods for the aortic heart valve and its replacements

INTRODUCTION

Replacement with a prosthetic device remains a major treatment option for the patients suffering from heart valve disease, with prevalence growing resulting from an ageing population. While the most popular replacement heart valve continues to be the bioprosthetic heart valve (BHV), its durability remains limited. There is thus a continued need to develop a general understanding of the underlying mechanisms limiting BHV durability to facilitate development of a more durable prosthesis. In this regard, computational models can play a pivotal role as they can evaluate our understanding of the underlying mechanisms and be used to optimize designs that may not always be intuitive. Areas covered: This review covers recent progress in computational models for the simulation of BHV, with a focus on aortic valve (AV) replacement. Recent contributions in valve geometry, leaflet material models, novel methods for numerical simulation, and applications to BHV optimization are discussed. This information should serve not only to infer reliable and dependable BHV function, but also to establish guidelines and insight for the design of future prosthetic valves by analyzing the influence of design, hemodynamics and tissue mechanics. Expert commentary: The paradigm of predictive modeling of heart valve prosthesis are becoming a reality which can simultaneously improve clinical outcomes and reduce costs. It can also lead to patient-specific valve design.

Mitral valve leaflet remodelling during pregnancy: insights into cell-mediated recovery of tissue homeostasis

Little is known about how valvular tissues grow and remodel in response to altered loading. In this work, we used the pregnancy state to represent a non-pathological cardiac volume overload that distends the mitral valve (MV), using both extant and new experimental data and a modified form of our MV structural constitutive model. We determined that there was an initial period of permanent set-like deformation where no remodelling occurs, followed by a remodelling phase that resulted in near-complete restoration of homeostatic tissue-level behaviour. In addition, we observed that changes in the underlying MV interstitial cell (MVIC) geometry closely paralleled the tissue-level remodelling events, undergoing an initial passive perturbation followed by a gradual recovery to the pre-pregnant state. Collectively, these results suggest that valvular remodelling is actively mediated by average MVIC deformations (i.e. not cycle to cycle, but over a period of weeks). Moreover, tissue-level remodelling is likely to be accomplished by serial and parallel additions of fibrillar material to restore the mean homeostatic fibre stress and MVIC geometries. This finding has significant implications in efforts to understand and predict MV growth and remodelling following such events as myocardial infarction and surgical repair, which also place the valve under altered loading conditions.

Modeling the response of exogenously crosslinked tissue to cyclic loading: The effects of permanent set

Bioprosthetic heart valves (BHVs), fabricated from exogenously crosslinked collagenous tissues, remain the most popular heart valve replacement design. However, the life span of BHVs remains limited to 10-15 years, in part because the mechanisms that underlie BHV failure remain poorly understood. Experimental evidence indicates that BHVs undergo significant changes in geometry with in vivo operation, which lead to stress concentrations that can have significant impact on structural damage. These changes do not appear to be due to plastic deformation, as the leaflets only deform in the elastic regime. Moreover, structural damage was not detected by the 65 million cycle time point. Instead, we found that this nonrecoverable deformation is similar to the permanent set effect observed in elastomers, which allows the reference configuration of the material to evolve over time. We hypothesize that the scission-healing reaction of glutaraldehyde is the underlying mechanism responsible for permanent set in exogenously crosslinked soft tissues. The continuous scission-healing process of glutaraldehyde allows a portion of the exogenously crosslinked matrix, which is considered to be the non-fibrous part of the extra-cellular matrix, to be re-crosslinked in the loaded state. Thus, this mechanism for permanent set can be used to explain the time evolving mechanical response and geometry of BHVs in the early stage. To model the permanent set effect, we assume that the exogenously crosslinked matrix undergoes changes in reference configurations over time. The changes in the collagen fiber architecture due to dimensional changes allow us to predict subsequent changes in mechanical response. Results show that permanent set alone can explain and, more importantly, predict how the mechanical response of the biomaterial change with time. Furthermore, we found is no difference in permanent set rate constants between the strain controlled and the stress controlled cyclic loading studies. An important finding we have is that the collagen fiber architecture has a limiting effect on the maximum changes in geometry that the permanent set effect can induce. This is due to the recruitment of collagen fibers as the changes in geometry due to permanent set increase. This means we can potentially optimize the BHV geometry based on the predicted the final BHV geometry after permanent set has largely ceased. Thus, we have developed the first structural constitutive model for the permanent set effect in exogenously crosslinked soft tissue, which can help to simulate BHV designs and reduce changes in BHV geometry during cyclic loading and thus potentially increasing BHV durability.

On the in vivo function of the mitral heart valve leaflet: insights into tissue-interstitial cell biomechanical coupling

There continues to be a critical need for developing data-informed computational modeling techniques that enable systematic evaluations of mitral valve (MV) function. This is important for a better understanding of MV organ-level biomechanical performance, in vivo functional tissue stresses, and the biosynthetic responses of MV interstitial cells (MVICs) in the normal, pathophysiological, and surgically repaired states. In the present study, we utilized extant ovine MV population-averaged 3D fiducial marker data to quantify the MV anterior leaflet (MVAL) deformations in various kinematic states. This approach allowed us to make the critical connection between the in vivo functional and the in vitro experimental configurations. Moreover, we incorporated the in vivo MVAL deformations and pre-strains into an enhanced inverse finite element modeling framework (Path 1) to estimate the resulting in vivo tissue prestresses [Formula: see text] and the in vivo peak functional tissue stresses [Formula: see text]. These in vivo stress estimates were then cross-verified with the results obtained from an alternative forward modeling method (Path 2), by taking account of the changes in the in vitro and in vivo reference configurations. Moreover, by integrating the tissue-level kinematic results into a downscale MVIC microenvironment FE model, we were able to estimate, for the first time, the in vivo layer-specific MVIC deformations and deformation rates of the normal and surgically repaired MVALs. From these simulations, we determined that the placement of annuloplasty ring greatly reduces the peak MVIC deformation levels in a layer-specific manner. This suggests that the associated reductions in MVIC deformation may down-regulate MV extracellular matrix maintenance, ultimately leading to reduction in tissue mechanical integrity. These simulations provide valuable insight into MV cellular mechanobiology in response to organ- and tissue-level alternations induced by MV disease or surgical repair. They will also assist in the future development of computer simulation tools for guiding MV surgery procedure with enhanced durability and improved long-term surgical outcomes.

A mathematical model for the determination of forming tissue moduli in needled-nonwoven scaffolds

Formation of engineering tissues (ET) remains an important scientific area of investigation for both clinical translational and mechanobiological studies. Needled-nonwoven (NNW) scaffolds represent one of the most ubiquitous biomaterials based on their well-documented capacity to sustain tissue formation and the unique property of substantial construct stiffness amplification, the latter allowing for very sensitive determination of forming tissue modulus. Yet, their use in more fundamental studies is hampered by the lack of: (1) substantial understanding of the mechanics of the NNW scaffold itself under finite deformations and means to model the complex mechanical interactions between scaffold fibers, cells, and de novo tissue; and (2) rational models with reliable predictive capabilities describing their evolving mechanical properties and their response to mechanical stimulation. Our objective is to quantify the mechanical properties of the forming ET phase in constructs that utilize NNW scaffolds. We present herein a novel mathematical model to quantify their stiffness based on explicit considerations of the modulation of NNW scaffold fiber-fiber interactions and effective fiber stiffness by surrounding de novo ECM. Specifically, fibers in NNW scaffolds are effectively stiffer than if acting alone due to extensive fiber-fiber cross-over points that impart changes in fiber geometry, particularly crimp wavelength and amplitude. Fiber-fiber interactions in NNW scaffolds also play significant role in the bulk anisotropy of the material, mainly due to fiber buckling and large translational out-of-plane displacements occurring to fibers undergoing contraction. To calibrate the model parameters, we mechanically tested impregnated NNW scaffolds with polyacrylamide (PAM) gels with a wide range of moduli with values chosen to mimic the effects of surrounding tissues on the scaffold fiber network. Results indicated a high degree of model fidelity over a wide range of planar strains. Lastly, we illustrated the impact of our modeling approach quantifying the stiffness of engineered ECM after in vitro incubation and early stages of in vivo implantation obtained in a concurrent study of engineered tissue pulmonary valves in an ovine model.

STATEMENT OF SIGNIFICANCE

Regenerative medicine has the potential to fully restore diseased tissues or entire organs with engineered tissues. Needled-nonwoven scaffolds can be employed to serve as the support for their growth. However, there is a lack of understanding of the mechanics of these materials and their interactions with the forming tissues. We developed a mathematical model for these scaffold-tissue composites to quantify the mechanical properties of the forming tissues. Firstly, these measurements are pivotal to achieve functional requirements for tissue engineering implants; however, the theoretical development yielded critical insight into particular mechanisms and behaviors of these scaffolds that were not possible to conjecture without the insight given by modeling, let alone describe or foresee a priori.

A novel constitutive model for passive right ventricular myocardium: evidence for myofiber-collagen fiber mechanical coupling

The function of right ventricle (RV) is recognized to play a key role in the development of many cardiopulmonary disorders, such as pulmonary arterial hypertension (PAH). Given the strong link between tissue structure and mechanical behavior, there remains a need for a myocardial constitutive model that accurately accounts for right ventricular myocardium architecture. Moreover, most available myocardial constitutive models approach myocardium at the length scale of mean fiber orientation and do not explicitly account for different fibrous constituents and possible interactions among them. In the present work, we developed a fiber-level constitutive model for the passive mechanical behavior of the right ventricular free wall (RVFW). The model explicitly separates the mechanical contributions of myofiber and collagen fiber ensembles, and accounts for the mechanical interactions between them. To obtain model parameters for the healthy passive RVFW, the model was informed by transmural orientation distribution measurements of myo- and collagen fibers and was fit to the mechanical testing data, where both sets of data were obtained from recent experimental studies on non-contractile, but viable, murine RVFW specimens. Results supported the hypothesis that in the low-strain regime, the behavior of the RVFW is governed by myofiber response alone, which does not demonstrate any coupling between different myofiber ensembles. At higher strains, the collagen fibers and their interactions with myofibers begin to gradually contribute and dominate the behavior as recruitment proceeds. Due to the use of viable myocardial tissue, the contribution of myofibers was significant at all strains with the predicted tensile modulus of [Formula: see text]32 kPa. This was in contrast to earlier reports (Horowitz et al. 1988) where the contribution of myofibers was found to be insignificant. Also, we found that the interaction between myo- and collagen fibers was greatest under equibiaxial strain, with its contribution to the total stress not exceeding 20 %. The present model can be applied to organ-level computational models of right ventricular dysfunction for efficient diagnosis and evaluation of pulmonary hypertension disorder.

Biomechanical Behavior of Bioprosthetic Heart Valve Heterograft Tissues: Characterization, Simulation, and Performance

The use of replacement heart valves continues to grow due to the increased prevalence of valvular heart disease resulting from an ageing population. Since bioprosthetic heart valves (BHVs) continue to be the preferred replacement valve, there continues to be a strong need to develop better and more reliable BHVs through and improved the general understanding of BHV failure mechanisms. The major technological hurdle for the lifespan of the BHV implant continues to be the durability of the constituent leaflet biomaterials, which if improved can lead to substantial clinical impact. In order to develop improved solutions for BHV biomaterials, it is critical to have a better understanding of the inherent biomechanical behaviors of the leaflet biomaterials, including chemical treatment technologies, the impact of repetitive mechanical loading, and the inherent failure modes. This review seeks to provide a comprehensive overview of these issues, with a focus on developing insight on the mechanisms of BHV function and failure. Additionally, this review provides a detailed summary of the computational biomechanical simulations that have been used to inform and develop a higher level of understanding of BHV tissues and their failure modes. Collectively, this information should serve as a tool not only to infer reliable and dependable prosthesis function, but also to instigate and facilitate the design of future bioprosthetic valves and clinically impact cardiology.

Tissue constructs: platforms for basic research and drug discovery

The functions, form and mechanical properties of cells are inextricably linked to their extracellular environment. Cells from solid tissues change fundamentally when, isolated from this environment, they are cultured on rigid two-dimensional substrata. These changes limit the significance of mechanical measurements on cells in two-dimensional culture and motivate the development of constructs with cells embedded in three-dimensional matrices that mimic the natural tissue. While measurements of cell mechanics are difficult in natural tissues, they have proven effective in engineered tissue constructs, especially constructs that emphasize specific cell types and their functions, e.g. engineered heart tissues. Tissue constructs developed as models of disease also have been useful as platforms for drug discovery. Underlying the use of tissue constructs as platforms for basic research and drug discovery is integration of multiscale biomaterials measurement and computational modelling to dissect the distinguishable mechanical responses separately of cells and extracellular matrix from measurements on tissue constructs and to quantify the effects of drug treatment on these responses. These methods and their application are the main subjects of this review.

Integrated multiscale biomaterials experiment and modelling: a perspective

Advances in multiscale models and computational power have enabled a broad toolset to predict how molecules, cells, tissues and organs behave and develop. A key theme in biological systems is the emergence of macroscale behaviour from collective behaviours across a range of length and timescales, and a key element of these models is therefore hierarchical simulation. However, this predictive capacity has far outstripped our ability to validate predictions experimentally, particularly when multiple hierarchical levels are involved. The state of the art represents careful integration of multiscale experiment and modelling, and yields not only validation, but also insights into deformation and relaxation mechanisms across scales. We present here a sampling of key results that highlight both challenges and opportunities for integrated multiscale experiment and modelling in biological systems.