Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the left ventricle

Segmenting mechanically heterogeneous domains via unsupervised learning

From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work toward developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clustering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code are published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.

Untangling the mechanisms of pulmonary arterial hypertension-induced right ventricular stiffening in a large animal model

Pulmonary hypertension (PHT) is a devastating disease with low survival rates. In PHT, chronic pressure overload leads to right ventricle (RV) stiffening; thus, impeding diastolic filling. Multiple mechanisms may contribute to RV stiffening, including wall thickening, microstructural disorganization, and myocardial stiffening. The relative importance of each mechanism is unclear. Our objective is to use a large animal model to untangle these mechanisms. Thus, we induced pulmonary arterial hypertension (PAH) in sheep via pulmonary artery banding. After eight weeks, the hearts underwent anatomic and diffusion tensor MRI to characterize wall thickening and microstructural disorganization. Additionally, myocardial samples underwent histological and gene expression analyses to quantify compositional changes and mechanical testing to quantify myocardial stiffening. Finally, we used finite element modeling to disentangle the relative importance of each stiffening mechanism. We found that the RVs of PAH animals thickened most at the base and the free wall and that PAH induced excessive collagen synthesis, increased cardiomyocyte cross-sectional area, and led to microstructural disorganization, consistent with increased expression of fibrotic genes. We also found that the myocardium itself stiffened significantly. Importantly, myocardial stiffening correlated significantly with collagen synthesis. Finally, our computational models predicted that myocardial stiffness contributes to RV stiffening significantly more than other mechanisms. Thus, myocardial stiffening may be the most important predictor for PAH progression. Given the correlation between myocardial stiffness and collagen synthesis, collagen-sensitive imaging modalities may be useful for estimating myocardial stiffness and predicting PAH outcomes. STATEMENT OF SIGNIFICANCE: Ventricular stiffening is a significant contributor to pulmonary hypertension-induced right heart failure. However, the mechanisms that lead to ventricular stiffening are not fully understood. The novelty of our work lies in answering this question through the use of a large animal model in combination with spatially- and directionally sensitive experimental techniques. We find that myocardial stiffness is the primary mechanism that leads to ventricular stiffening. Clinically, this knowledge may be used to improve diagnostic, prognostic, and therapeutic strategies for patients with pulmonary hypertension.

A New Strain Energy Function Representing the Passive Behavior of the Myocardium

Classical models for the passive myocardium, such as the Fung and Holzapfel-Ogden models, are known to have high degeneracy as well as numerous mechanical and mathematical limitations, preventing their utility in microstructural experiments and precision medicine. Hence, the upper triangular (QR) decomposition and orthogonal strain attributes were leveraged to develop a new model using published biaxial data on slabs of left myocardium, resulting in a separable strain energy function. This new model, the Criscione-Hussein model, was compared with both the Fung and Holzapfel-Ogden models by quantifying the uncertainty, computational efficiency, and material parameter fidelity for all three models. As a result, the Criscione-Hussein model was found to significantly reduce the uncertainty and computational time (p < 0.05) and enhance the fidelity of the material parameters. Hence, the Criscione-Hussein model enhances the predictability for the passive behavior of the myocardium and may serve a role in creating more accurate computational models that provide better visualizations for the mechanical behavior of the heart and enable the experimental connection between the model and the myocardial microstructure.

Quantifying the microstructural and biomechanical changes in the porcine ventricles during growth and remodelling

Cardiac tissue growth and remodelling (G & R) occur in response to the changing physiological demands of the heart after birth. The early shift to pulmonary circulation produces an immediate increase in ventricular workload, causing microstructural and biomechanical changes that serve to maintain overall physiological homoeostasis. Such cardiac G & R continues throughout life. Quantifying the tissue's mechanical and microstructural changes because of G & R is of increasing interest, dovetailing with the emerging fields of personalised and precision solutions. This study aimed to determine equibiaxial, and non-equibiaxial extension, stress-relaxation, and the underlying microstructure of the passive porcine ventricles tissue at four time points spanning from neonatal to adulthood. The three-dimensional microstructure was investigated via two-photon excited fluorescence and second-harmonic generation microscopy on optically cleared tissues, describing the 3D orientation, rotation and dispersion of the cardiomyocytes and collagen fibrils. The results revealed that during biomechanical testing, myocardial ventricular tissue possessed non-linear, anisotropic, and viscoelastic behaviour. An increase in stiffness and viscoelasticity was noted for the left and right ventricular free walls from neonatal to adulthood. Microstructural analyses revealed concomitant increases in cardiomyocyte rotation and dispersion. This study provides baseline data, describing the biomechanical and microstructural changes in the left and right ventricular myocardial tissue during G & R, which should prove valuable to researchers in developing age-specific, constitutive models for more accurate computational simulations. STATEMENT OF SIGNIFICANCE: There is a dearth of experimental data describing the growth and remodelling of left and right ventricular tissue. The published literature is fragmented, with data reported via different experimental techniques using tissues harvested from a variety of animals, with different gender and ages. This prevents developing a continuum of data spanning birth to death, so limiting the potential that can be leveraged to aid computational modelling and simulations. In this study, equibiaxial, non-equibiaxial, and stress-relaxation data are presented, describing directional-dependent material responses. The biomechanical data is consolidated with equivalent microstructural data, an important element for the development of future material models. Combined, these data describe microstructural and biomechanical changes in the ventricles, spanning G &R from neonatal to adulthood.

Optimal assist strategy exploration for a direct assist device under stress‒strain dynamics

OBJECTIVES

The aim of this paper is to introduce a new assist strategy for a direct assist device that can enhance the heart's pumping efficiency and decrease the chances of myocardial injury in contrast to the conventional assist strategy.

METHODS

We established a finite element model of a biventricular heart, divided the ventricles into several regions, and applied pressure to each region separately in order to identify the primary and secondary assist areas. Then combined and tested these areas to obtain the optimal assist strategy.

RESULTS

The results indicate that our method exhibits an assist efficiency approximately ten times higher than that of the traditional assist method. Additionally, the stress distribution in the ventricles is more uniform after assistance.

CONCLUSIONS

In summary, this approach can result in a more homogenous stress distribution within the heart while also minimizing the contact area with it, which can reduce the incidence of allergic reactions and the likelihood of myocardial injury.

AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load-displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load-displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.

A multi-scale computational model for the passive mechanical behavior of right ventricular myocardium

We have previously demonstrated the importance of myofiber-collagen mechanical interactions in modeling the passive mechanical behavior of right ventricle free wall (RVFW) myocardium. To gain deeper insights into these coupling mechanisms, we developed a high-fidelity, micro-anatomically realistic 3D finite element model of right ventricle free wall (RVFW) myocardium by combining high-resolution imaging and supercomputer-based simulations. We first developed a representative tissue element (RTE) model at the sub-tissue scale by specializing the hyperelastic anisotropic structurally-based constitutive relations for myofibers and ECM collagen, and equi-biaxial and non-equibiaxial loading conditions were simulated using the open-source software FEniCS to compute the effective stress-strain response of the RTE. To estimate the model parameters of the RTE model, we first fitted a 'top-down' biaxial stress-strain behavior with our previous structurally based (tissue-scale) model, informed by the measured myofiber and collagen fiber composition and orientation distributions. Next, we employed a multi-scale approach to determine the tissue-level (5 x 5 x 0.7 mm specimen size) RVFW biaxial behavior via 'bottom-up' homogenization of the fitted RTE model, recapitulating the histologically measured myofiber and collagen orientation to the biaxial mechanical data. Our homogenization approach successfully reproduced the tissue-level mechanical behavior of our previous studies in all biaxial deformation modes, suggesting that the 3D micro-anatomical arrangement of myofibers and ECM collagen is indeed a primary mechanism driving myofiber-collagen interactions.

Untangling the mechanisms of pulmonary hypertension-induced right ventricular stiffening in a large animal model

Background

Pulmonary arterial hypertension (PHT) is a devastating disease with low survival rates. In PHT, chronic pressure overload leads to right ventricle (RV) remodeling and stiffening; thus, impeding diastolic filling and ventricular function. Multiple mechanisms contribute to RV stiffening, including wall thickening, microstructural disorganization, and myocardial stiffening. The relative importance of each mechanism is unclear. Our objective is to use a large animal model as well as imaging, experimental, and computational approaches to untangle these mechanisms.

Methods

We induced PHT in eight sheep via pulmonary artery banding. After eight weeks, the hearts underwent anatomic and diffusion tensor MRI to characterize wall thickening and microstructural disorganization. Additionally, myocardial samples underwent histological and gene expression analyses to quantify compositional changes and mechanical testing to quantify myocardial stiffening. All findings were compared to 12 control animals. Finally, we used computational modeling to disentangle the relative importance of each stiffening mechanism.

Results

First, we found that the RVs of PHT animals thickened most at the base and the free wall. Additionally, we found that PHT induced excessive collagen synthesis and microstructural disorganization, consistent with increased expression of fibrotic genes. We also found that the myocardium itself stiffened significantly. Importantly, myocardial stiffening correlated significantly with excess collagen synthesis. Finally, our model of normalized RV pressure-volume relationships predicted that myocardial stiffness contributes to RV stiffening significantly more than other mechanisms.

Conclusions

In summary, we found that PHT induces wall thickening, microstructural disorganization, and myocardial stiffening. These remodeling mechanisms were both spatially and directionally dependent. Using modeling, we show that myocardial stiffness is the primary contributor to RV stiffening. Thus, myocardial stiffening may be an important predictor for PHT progression. Given the significant correlation between myocardial stiffness and collagen synthesis, collagen-sensitive imaging modalities may be useful for non-invasively estimating myocardial stiffness and predicting PHT outcomes.

The multiscale characterization and constitutive modeling of healthy and type 2 diabetes mellitus Sprague Dawley rat skin

In type 2 diabetes mellitus (T2DM), elevated glucose level impairs the biochemistry of the skin which may result in alteration of its mechanical and structural properties. The several aspects of structural and mechanical changes in skin due to T2DM remain poorly understood. To fill these research gaps, we developed a non-obese T2DM rat (Sprague Dawley (SD)) model for investigating the effect of T2DM on the in vivo strain stress state, mechanical and structural properties of skin. In vivo strain and mechanical anisotropy of healthy and T2DM skin were measured using the digital imaging correlation (DIC) technique and DIC coupled bulge experiment, respectively. Fluorescence microscopy and histology were used to assess the collagen and elastin fibers microstructure whereas nanoscale structure was captured through atomic force microscopy (AFM). Based on the microstructural observations, skin was modeled as a multilayer membrane where in and out of plane distribution of collagen fibers and planar distribution of elastin fibers were cast in constitutive model. Further, the state of in vivo stresses of healthy and T2DM were measured using model parameters and in vivo strain in the constitutive model. The results showed that T2DM causes significant loss in in vivo stresses (p < 0.01) and increase in anisotropy (p < 0.001) of skin. These changes were found in good correlation with T2DM associated alteration in skin microstructure. Statistical analysis emphasized that increase in blood glucose concentration (HbA1c) was the main cause of impaired biomechanical properties of skin. The presented data in this study can help to understand the skin pathology and to simulate the skin related clinical procedures. STATEMENT OF SIGNIFICANCE: Our study is significant as it presents findings related to the effect of T2DM on the physiologic stress strain, structural and mechanical response of SD rat skin. In this study, we developed a non-obese T2DM SD rat model which mimics the phenotype of Asian type 2 diabetics (non-obese). Several structural and mechanical characterization techniques were explored for multiscale characterization of healthy and T2DM skin. Further, based on microstructural information, we presented the constitutive models that incorporate the real microstructure of skin. The presented results can be helpful to simulate the realistic mechanical response of skin during various clinical trials.

Can machine learning accelerate soft material parameter identification from complex mechanical test data?

Identifying the constitutive parameters of soft materials often requires heterogeneous mechanical test modes, such as simple shear. In turn, interpreting the resulting complex deformations necessitates the use of inverse strategies that iteratively call forward finite element solutions. In the past, we have found that the cost of repeatedly solving non-trivial boundary value problems can be prohibitively expensive. In this current work, we leverage our prior experimentally derived mechanical test data to explore an alternative approach. Specifically, we investigate whether a machine learning-based approach can accelerate the process of identifying material parameters based on our mechanical test data. Toward this end, we pursue two different strategies. In the first strategy, we replace the forward finite element simulations within an iterative optimization framework with a machine learning-based metamodel. Here, we explore both Gaussian process regression and neural network metamodels. In the second strategy, we forgo the iterative optimization framework and use a stand alone neural network to predict the entire material parameter set directly from experimental results. We first evaluate both approaches with simple shear experiments on blood clot, an isotropic, homogeneous material. Next, we evaluate both approaches against simple shear and uniaxial loading experiments on right ventricular myocardium, an anisotropic, heterogeneous material. We find that replacing the forward finite element simulations with metamodels significantly accelerates the parameter identification process with excellent results in the case of blood clot, and with satisfying results in the case of right ventricular myocardium. On the other hand, we find that replacing the entire optimization framework with a neural network yielded unsatisfying results, especially for right ventricular myocardium. Overall, the importance of our work stems from providing a baseline example showing how machine learning can accelerate the process of material parameter identification for soft materials from complex mechanical data, and from providing an open access experimental and simulation dataset that may serve as a benchmark dataset for others interested in applying machine learning techniques to soft tissue biomechanics.

A cell-based framework for modeling cardiac mechanics

Cardiomyocytes are the functional building blocks of the heart-yet most models developed to simulate cardiac mechanics do not represent the individual cells and their surrounding matrix. Instead, they work on a homogenized tissue level, assuming that cellular and subcellular structures and processes scale uniformly. Here we present a mathematical and numerical framework for exploring tissue-level cardiac mechanics on a microscale given an explicit three-dimensional geometrical representation of cells embedded in a matrix. We defined a mathematical model over such a geometry and parametrized our model using publicly available data from tissue stretching and shearing experiments. We then used the model to explore mechanical differences between the extracellular and the intracellular space. Through sensitivity analysis, we found the stiffness in the extracellular matrix to be most important for the intracellular stress values under contraction. Strain and stress values were observed to follow a normal-tangential pattern concentrated along the membrane, with substantial spatial variations both under contraction and stretching. We also examined how it scales to larger size simulations, considering multicellular domains. Our work extends existing continuum models, providing a new geometrical-based framework for exploring complex cell-cell and cell-matrix interactions.

An introduction to the Ogden model in biomechanics: benefits, implementation tools and limitations

Constitutive models are important to biomechanics for two key reasons. First, constitutive modelling is an essential component of characterizing tissues' mechanical properties for informing theoretical and computational models of biomechanical systems. Second, constitutive models can be used as a theoretical framework for extracting and comparing key quantities of interest from material characterization experiments. Over the past five decades, the Ogden model has emerged as a popular constitutive model in soft tissue biomechanics with relevance to both informing theoretical and computational models and to comparing material characterization experiments. The goal of this short review is threefold. First, we will discuss the broad relevance of the Ogden model to soft tissue biomechanics and the general characteristics of soft tissues that are suitable for approximating with the Ogden model. Second, we will highlight exemplary uses of the Ogden model in brain tissue, blood clot and other tissues. Finally, we offer a tutorial on fitting the one-term Ogden model to pure shear experimental data via both an analytical approximation of homogeneous deformation and a finite-element model of the tissue domain. Overall, we anticipate that this short review will serve as a practical introduction to the use of the Ogden model in biomechanics. This article is part of the theme issue 'The Ogden model of rubber mechanics: Fifty years of impact on nonlinear elasticity'.

An introduction to the Ogden model in biomechanics: benefits, implementation tools and limitations

Constitutive models are important to biomechanics for two key reasons. First, constitutive modelling is an essential component of characterizing tissues' mechanical properties for informing theoretical and computational models of biomechanical systems. Second, constitutive models can be used as a theoretical framework for extracting and comparing key quantities of interest from material characterization experiments. Over the past five decades, the Ogden model has emerged as a popular constitutive model in soft tissue biomechanics with relevance to both informing theoretical and computational models and to comparing material characterization experiments. The goal of this short review is threefold. First, we will discuss the broad relevance of the Ogden model to soft tissue biomechanics and the general characteristics of soft tissues that are suitable for approximating with the Ogden model. Second, we will highlight exemplary uses of the Ogden model in brain tissue, blood clot and other tissues. Finally, we offer a tutorial on fitting the one-term Ogden model to pure shear experimental data via both an analytical approximation of homogeneous deformation and a finite-element model of the tissue domain. Overall, we anticipate that this short review will serve as a practical introduction to the use of the Ogden model in biomechanics. This article is part of the theme issue 'The Ogden model of rubber mechanics: Fifty years of impact on nonlinear elasticity'.

Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue

Closed-form constitutive models are the standard to describe soft tissue mechanical behavior. However, inherent pitfalls of an explicit functional form include poor fits to the data, non-uniqueness of fit, and sensitivity to parameters. Here we design deep neural networks (DNN) that satisfy desirable physics constraints in order to replace expert models of tissue mechanics. To guarantee stress-objectivity, the DNN takes strain (pseudo)-invariants as inputs, and outputs the strain energy and its derivatives. Polyconvexity of strain energy is enforced through the loss function. Direct prediction of both energy and derivative functions enables the computation of the elasticity tensor needed for a finite element implementation. We showcase the DNN ability to learn the anisotropic mechanical behavior of porcine and murine skin from biaxial test data. A multi-fidelity scheme that combines high fidelity experimental data with a low fidelity analytical approximation yields the best performance. Finite element simulations of tissue expansion with the DNN model illustrate the potential of this method to impact medical device design for skin therapeutics. We expect that the open data and software from this work will broaden the use of data-driven constitutive models of tissue mechanics.

Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset

Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize, and difficult to simulate. Recently, machine learning (ML)-based methods to predict the mechanical behavior of heterogeneous materials have made it possible to more thoroughly explore the massive input parameter space associated with heterogeneous blocks of material. Specifically, we can train ML models to closely approximate computationally expensive heterogeneous material simulations where the ML model is trained on datasets of simulations with relevant spatial heterogeneity. However, when it comes to applying these techniques to tissue, there is a major limitation: the number of useful examples available to characterize the input domain under study is often limited. In this work, we investigate the efficacy of both ML-based generative models and procedural methods as tools for augmenting limited input pattern datasets. We find that a Style-based Generative Adversarial Network with an adaptive discriminator augmentation mechanism is able to successfully leverage just 1,000 example patterns to create authentic generated patterns. And, we find that diverse generated patterns with adequate resemblance to real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset. To enable this methodological contribution, we have created an open access Finite Element Analysis simulation dataset based on Cahn-Hilliard patterns. We anticipate that future researchers will be able to leverage this dataset and build on the work presented here.

Multiscale Contrasts Between the Right and Left Ventricle Biomechanics in Healthy Adult Sheep and Translational Implications

Cardiac biomechanics play a significant role in the progression of structural heart diseases (SHDs). SHDs alter baseline myocardial biomechanics leading to single or bi-ventricular dysfunction. But therapies for left ventricle (LV) failure patients do not always work well for right ventricle (RV) failure patients. This is partly because the basic knowledge of baseline contrasts between the RV and LV biomechanics remains elusive with limited discrepant findings. The aim of the study was to investigate the multiscale contrasts between LV and RV biomechanics in large animal species. We hypothesize that the adult healthy LV and RV have distinct passive anisotropic biomechanical properties. Ex vivo biaxial tests were performed in fresh sheep hearts. Histology and immunohistochemistry were performed to measure tissue collagen. The experimental data were then fitted to a Fung type model and a structurally informed model, separately. We found that the LV was stiffer in the longitudinal (outflow tract) than circumferential direction, whereas the RV showed the opposite anisotropic behavior. The anisotropic parameter K from the Fung type model accurately captured contrasting anisotropic behaviors in the LV and RV. When comparing the elasticity in the same direction, the LV was stiffer than the RV longitudinally and the RV was stiffer than the LV circumferentially, suggesting different filling patterns of these ventricles during diastole. Results from the structurally informed model suggest potentially stiffer collagen fibers in the LV than RV, demanding further investigation. Finally, type III collagen content was correlated with the low-strain elastic moduli in both ventricles. In summary, our findings provide fundamental biomechanical differences between the chambers. These results provide valuable insights for guiding cardiac tissue engineering and regenerative studies to implement chamber-specific matrix mechanics, which is particularly critical for identifying biomechanical mechanisms of diseases or mechanical regulation of therapeutic responses. In addition, our results serve as a benchmark for image-based inverse modeling technologies to non-invasively estimate myocardial properties in the RV and LV.

Molecular Changes in Prepubertal Left Ventricular Development Under Experimental Volume Overload

Background

Left ventricular (LV) volume overload (VO), commonly found in patients with chronic aortic regurgitation (AR), leads to a series of left ventricular (LV) pathological responses and eventually irreversible LV dysfunction. Recently, questions about the applicability of the guideline for the optimal timing of valvular surgery to correct chronic AR have been raised in regard to both adult and pediatric patients. Understanding how VO regulates postnatal LV development may shed light on the best timing of surgical or drug intervention.

Methods and Results

Prepubertal LV VO was induced by aortocaval fistula (ACF) on postnatal day 7 (P7) in mice. LV free walls were analyzed on P14 and P21. RNA-sequencing analysis demonstrated that normal (P21_Sham vs.P14_Sham) and VO-influenced (P21_VO vs. P14_VO) LV development shared common terms of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) in the downregulation of cell cycle activities and the upregulation of metabolic and sarcomere maturation. The enriched GO terms associated with cardiac condition were only observed in normal LV development, while the enriched GO terms associated with immune responses were only observed in VO-influenced LV development. These results were further validated by the examination of the markers of cell cycle, maturation, and immune responses. When normal and VO-influenced LVs of P21 were compared, they were different in terms of immune responses, angiogenesis, percentage of Ki67-positive cardiomyocytes, mitochondria number, T-tubule regularity, and sarcomere regularity and length.

Conclusions

A prepubertal LV VO mouse model was first established. VO has an important influence on LV maturation and development, especially in cardiac conduction, suggesting the requirement of an early correction of AR in pediatric patients. The underlying mechanism may be associated with the activation of immune responses.

Determination of Cross-Directional and Cross-Wall Variations of Passive Biaxial Mechanical Properties of Rat Myocardia

Heart myocardia are critical to the facilitation of heart pumping and blood circulating around the body. The biaxial mechanical testing of the left ventricle (LV) has been extensively utilised to build the computational model of the whole heart with little importance given to the unique mechanical properties of the right ventricle (RV) and cardiac septum (SPW). Most of those studies focussed on the LV of the heart and then applied the obtained characteristics with a few modifications to the right side of the heart. However, the assumption that the LV characteristics applies to the RV has been contested over time with the realisation that the right side of the heart possesses its own unique mechanical properties that are widely distinct from that of the left side of the heart. This paper evaluates the passive mechanical property differences in the three main walls of the rat heart based on biaxial tensile test data. Fifteen mature Wistar rats weighing 225 ± 25 g were euthanised by inhalation of 5% halothane. The hearts were excised after which all the top chambers comprising the two atria, pulmonary and vena cava trunks, aorta, and valves were all dissected out. Then, 5 × 5 mm sections from the middle of each wall were carefully dissected with a surgical knife to avoid overly pre-straining the specimens. The specimens were subjected to tensile testing. The elastic moduli, peak stresses in the toe region and stresses at 40% strain, anisotropy indices, as well as the stored strain energy in the toe and linear region of up to 40% strain were used for statistical significance tests. The main findings of this study are: (1) LV and SPW tissues have relatively shorter toe regions of 10–15% strain as compared to RV tissue, whose toe region extends up to twice as much as that; (2) LV tissues have a higher strain energy storage in the linear region despite being lower in stiffness than the RV; and (3) the SPW has the highest strain energy storage along both directions, which might be directly related to its high level of anisotropy. These findings, though for a specific animal species at similar age and around the same body mass, emphasise the importance of the application of wall-specific material parameters to obtain accurate ventricular hyperelastic models. The findings further enhance our understanding of the desired mechanical behaviour of the different ventricle walls.

Synthetic hydrogels as blood clot mimicking wound healing materials

Excessive bleeding-or hemorrhage-causes millions of civilian and non-civilian casualties every year. Additionally, wound sequelae, such as infections, are a significant source of chronic morbidity, even if the initial bleeding is successfully stopped. To treat acute and chronic wounds, numerous wound healing materials have been identified, tested, and adopted. Among them are topical dressings, such as gauzes, as well as natural and biomimetic materials. However, none of these materials successfully mimic the complex and dynamic properties of the body's own wound healing material: the blood clot. Specifically, blood clots exhibit complex mechanical and biochemical properties that vary across spatial and temporal scales to guide the wound healing response, which make them the ideal wound healing material. In this manuscript, we review blood clots' complex mechanical and biochemical properties, review current wound healing materials, and identify opportunities where new materials can provide additional functionality, with a specific focus on hydrogels. We highlight recent developments in synthetic hydrogels that make them capable of mimicking a larger subset of blood clot features: as plugs and as stimuli for tissue repair. We conclude that future hydrogel materials designed to mimic blood clot biochemistry, mechanics, and architecture can be combined with exciting platelet-like particles to serve as hemostats that also promote the biological wound healing response. Thus, we believe synthetic hydrogels are ideal candidates to address the clear need for better wound healing materials.

Postnatal Right Ventricular Developmental Track Changed by Volume Overload

Background Current right ventricular (RV) volume overload (VO) is established in adult mice. There are no neonatal mouse VO models and how VO affects postnatal RV development is largely unknown. Methods and Results Neonatal VO was induced by the fistula between abdominal aorta and inferior vena cava on postnatal day 7 and confirmed by abdominal ultrasound, echocardiography, and hematoxylin and eosin staining. The RNA-sequencing results showed that the top 5 most enriched gene ontology terms in normal RV development were energy derivation by oxidation of organic compounds, generation of precursor metabolites and energy, cellular respiration, striated muscle tissue development, and muscle organ development. Under the influence of VO, the top 5 most enriched gene ontology terms were angiogenesis, regulation of cytoskeleton organization, regulation of vasculature development, regulation of mitotic cell cycle, and regulation of the actin filament-based process. The top 3 enriched signaling pathways for the normal RV development were PPAR signaling pathway, citrate cycle (Tricarboxylic acid cycle), and fatty acid degradation. VO changed the signaling pathways to focal adhesion, the PI3K-Akt signaling pathway, and pathways in cancer. The RNA sequencing results were confirmed by the examination of the markers of metabolic and cardiac muscle maturation and the markers of cell cycle and angiogenesis. Conclusions A neonatal mouse VO model was successfully established, and the main processes of postnatal RV development were metabolic and cardiac muscle maturation, and VO changed that to angiogenesis and cell cycle regulation.