Sex- and age-dependent skin mechanics-A detailed look in mice.

Skin aging is of immense societal and, thus, scientific interest. Because mechanics play a critical role in skin's function, a plethora of studies have investigated age-induced changes in skin mechanics. Nonetheless, much remains to be learned about the mechanics of aging skin. This is especially true when considering sex as a biological variable. In our work, we set out to answer some of these questions using mice as a model system. Specifically, we combined mechanical testing, histology, collagen assays, and two-photon microscopy to identify age- and sex-dependent changes in skin mechanics and to relate them to structural, microstructural, and compositional factors. Our work revealed that skin stiffness, thickness, and collagen content all decreased with age and were sex dependent. Interestingly, sex differences in stiffness were age induced. We hope our findings not only further our fundamental understanding of skin aging but also highlight both age and sex as important variables when conducting studies on skin mechanics. STATEMENT OF SIGNIFICANCE: Our work addresses the question, "How do sex and age affect the mechanics of skin?" Answering this question is of both scientific and societal importance. We do so in mice as a model system. Thereby, we hope to add clarity to a body of literature that appears divided on the effect of both factors. Our findings have important implications for those studying age and sex differences, especially in mice as a model system.

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.

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.

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.

Sex- and Age-dependent Skin Mechanics - A Detailed Look in Mice.

Skin aging is of immense societal and, thus, scientific interest. Because mechanics play a critical role in skin's function, a plethora of studies have investigated age-induced changes in skin mechanics. Nonetheless, much remains to be learned about the mechanics of aging skin. This is especially true when considering sex as a biological variable. In our work, we set out to answer some of these questions using mice as a model system. Specifically, we combined mechanical testing, histology, collagen assays, and two-photon microscopy to identify age- and sex-dependent changes in skin mechanics and to relate them to structural, microstructural, and compositional factors. Our work revealed that skin stiffness, thickness, and collagen content all decreased with age and were sex dependent. Interestingly, sex differences in stiffness were age induced. We hope our findings not only further our fundamental understanding of skin aging but also highlight both age and sex as important variables when conducting studies on skin mechanics.

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.

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'.

Hyper-viscoelastic damage modeling of whole blood clot under large deformation.

Blood clots play a diametric role in our bodies as they are both vital as a wound sealant, as well as the source for many devastating diseases. In blood clots' physiological and pathological roles, their mechanics play a critical part. These mechanics are non-trivial owing to blood clots' complex nonlinear, viscoelastic behavior. Casting this behavior into mathematical form is a fundamental step toward a better basic scientific understanding of blood clots, as well as toward diagnostic and prognostic computational models. Here, we identify a hyper-viscoelastic damage model that we fit to original data on the nonlinear, viscoelastic behavior of blood clots. Our model combines the classic Ogden hyperelastic constitutive law, a finite viscoelastic model for large deformations, and a non-local, gradient-enhanced damage formulation. By fitting our model to cyclic tensile test data and extension-to-failure data, we inform the model's nine unknown material parameters. We demonstrate the predictability of our model by validating it against unseen cyclic tensile test and stress-relaxation data. Our original data, model formulation, and the identified constitutive parameters of this model are openly available for others to use, which will aid in developing accurate, quantitative simulations of blood clot mechanics.

A whole blood thrombus mimic: Constitutive behavior under simple shear.

Deep vein thrombosis and pulmonary embolism affect 300,000-600,000 patients each year in the US. To better understand the highly mechanical pathophysiology of pulmonary embolism, we set out to develop an in-vitro thrombus mimic and to test this mimic under large deformation simple shear. In addition to reporting on the mechanics of our mimics under simple shear, we explore the sensitivity of their mechanics to coagulation conditions and blood storage time, and compare three hyperelastic material models for their ability to fit our data. We found that thrombus mimics made from whole blood demonstrate strain-stiffening, a negative Poynting effect, and hysteresis when tested quasi-statically to 50% strain under simple shear. Additionally, we found that the stiffness of these mimics does not significantly vary with coagulation conditions or blood storage times. Of the three hyperelastic constitutive models that we tested, the Ogden model provided the best fits to both shear stress and normal stress. In conclusion, we developed a robust protocol to generate regularly-shaped, homogeneous thrombus mimics that lend themselves to simple shear testing under large deformation. Future studies will extend our model to include the effect of maturation and explore its fracture properties toward a better understanding of embolization.

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

The right ventricular myocardium, much like the rest of the right side of the heart, has been consistently understudied. Presently, little is known about its mechanics, its microstructure, and its constitutive behavior. In this work, we set out to provide the first data on the mechanics of the mature right ventricular myocardium in both simple shear and uniaxial loading and to compare these data to the mechanics of the left ventricular myocardium. To this end, we tested ovine tissue samples of the right and left ventricle under a comprehensive mechanical testing protocol that consisted of six simple shear modes and three tension/compression modes. After mechanical testing, we conducted a histology-based microstructural analysis on each right ventricular sample that yielded high resolution fiber distribution maps across the entire samples. Equipped with this detailed mechanical and histological data, we employed an inverse finite element framework to determine the optimal form and parameters for microstructure-based constitutive models. The results of our study show that right ventricular myocardium is less stiff then the left ventricular myocardium in the fiber direction, but similarly exhibits non-linear, anisotropic, and tension/compression asymmetric behavior with direction-dependent Poynting effect. In addition, we found that right ventricular myocardial fibers change angles transmurally and are dispersed within the sheet plane and normal to it. Through our inverse finite element analysis, we found that the Holzapfel model successfully fits these data, even when selectively informed by rudimentary microstructural information. And, we found that the inclusion of higher-fidelity microstructural data improved the Holzapfel model's predictive ability. Looking forward, this investigation is a critical step towards understanding the fundamental mechanical behavior of right ventricular myocardium and lays the groundwork for future whole-organ mechanical simulations.