Right ventricular stiffening and anisotropy alterations in pulmonary hypertension: Mechanisms and relations to function.

Aims: Pulmonary hypertension (PH) results in an increase in RV afterload, leading to RV dysfunction and failure. The mechanisms underlying maladaptive RV remodeling are poorly understood. In this study, we investigated the multiscale and mechanistic nature of RV free wall (RVFW) biomechanical remodeling and its correlations with RV function adaptations. Methods and Results: Mild and severe models of PH, consisting of hypoxia (Hx) model in Sprague-Dawley (SD) rats (n=6 each, Control and PH) and Sugen-hypoxia (SuHx) model in Fischer (CDF) rats (n=6 each, Control and PH), were used. Organ-level function and tissue-level stiffness and microstructure were quantified through in-vivo and ex-vivo measures, respectively. Multiscale analysis was used to determine the association between fiber-level remodeling, tissue-level stiffening, and organ-level dysfunction. Animal models with different PH severity provided a wide range of RVFW stiffening and anisotropy alterations in PH. Decreased RV-pulmonary artery (PA) coupling correlated strongly with stiffening but showed a weaker association with the loss of RVFW anisotropy. Machine learning classification identified the range of adaptive and maladaptive RVFW stiffening. Multiscale modeling revealed that increased collagen fiber tautness was a key remodeling mechanism that differentiated severe from mild stiffening. Myofiber orientation analysis indicated a shift away from the predominantly circumferential fibers observed in healthy RVFW specimens, leading to a significant loss of tissue anisotropy. Conclusion: Multiscale biomechanical analysis indicated that although hypertrophy and fibrosis occur in both mild and severe PH, certain fiber-level remodeling events, including increased tautness in the newly deposited collagen fibers and significant reorientations of myofibers, contributed to excessive biomechanical maladaptation of the RVFW leading to severe RV-PA uncoupling. Collagen fiber remodeling and the loss of tissue anisotropy can provide an improved understanding of the transition from adaptive to maladaptive remodeling. Translational perspective: Right ventricular (RV) failure is a leading cause of mortality in patients with pulmonary hypertension (PH). RV diastolic and systolic impairments are evident in PH patients. Stiffening of the RV wall tissue and changes in the wall anisotropy are expected to be major contributors to both impairments. Global assessments of the RV function remain inadequate in identifying patients with maladaptive RV wall remodeling primarily due to their confounded and weak representation of RV fiber and tissue remodeling events. This study provides novel insights into the underlying mechanisms of RV biomechanical remodeling and identifies the adaptive-to-maladaptive transition across the RV biomechanics-function spectrum. Our analysis dissecting the contribution of different RV wall remodeling events to RV dysfunction determines the most adverse fiber-level remodeling to RV dysfunction as new therapeutic targets to curtail RV maladaptation and, in turn, RV failure in PH.

Targeted Rapamycin Delivery via Magnetic Nanoparticles to Address Stenosis in a 3D Bioprinted in Vitro Model of Pulmonary Veins.

Vascular cell overgrowth and lumen size reduction in pulmonary vein stenosis (PVS) can result in elevated PV pressure, pulmonary hypertension, cardiac failure, and death. Administration of chemotherapies such as rapamycin have shown promise by inhibiting the vascular cell proliferation; yet clinical success is limited due to complications such as restenosis and off-target effects. The lack of in vitro models to recapitulate the complex pathophysiology of PVS has hindered the identification of disease mechanisms and therapies. This study integrated 3D bioprinting, functional nanoparticles, and perfusion bioreactors to develop a novel in vitro model of PVS. Bioprinted bifurcated PV constructs are seeded with endothelial cells (ECs) and perfused, demonstrating the formation of a uniform and viable endothelium. Computational modeling identified the bifurcation point at high risk of EC overgrowth. Application of an external magnetic field enabled targeting of the rapamycin-loaded superparamagnetic iron oxide nanoparticles at the bifurcation site, leading to a significant reduction in EC proliferation with no adverse side effects. These results establish a 3D bioprinted in vitro model to study PV homeostasis and diseases, offering the potential for increased throughput, tunability, and patient specificity, to test new or more effective therapies for PVS and other vascular diseases.

An image-driven micromechanical approach to characterize multiscale remodeling in infarcted myocardium.

Myocardial infarction (MI) is accompanied by the formation of a fibrotic scar in the left ventricle (LV) and initiates significant alterations in the architecture and constituents of the LV free wall (LVFW). Previous studies have shown that LV adaptation is highly individual, indicating that the identification of remodeling mechanisms post-MI demands a fully subject-specific approach that can integrate a host of structural alterations at the fiber-level to changes in bulk biomechanical adaptation at the tissue-level. We present an image-driven micromechanical approach to characterize remodeling, assimilating new biaxial mechanical data, histological studies, and digital image correlation data within an in-silico framework to elucidate the fiber-level remodeling mechanisms that drive tissue-level adaptation for each subject. We found that a progressively diffused collagen fiber structure combined with similarly disorganized myofiber architecture in the healthy region leads to the loss of LVFW anisotropy post-MI, offering an important tissue-level hallmark for LV maladaptation. In contrast, our results suggest that reductions in collagen undulation are an adaptive mechanism competing against LVFW thinning. Additionally, we show that the inclusion of subject-specific geometry when modeling myocardial tissue is essential for accurate prediction of tissue kinematics. Our approach serves as an essential step toward identifying fiber-level remodeling indices that govern the transition of MI to systolic heart failure. These indices complement the traditional, organ-level measures of LV anatomy and function that often fall short of early prognostication of heart failure in MI. In addition, our approach offers an integrated methodology to advance the design of personalized interventions, such as hydrogel injection, to reinforce and suppress native adaptive and maladaptive mechanisms, respectively, to prevent the transition of MI to heart failure. STATEMENT OF SIGNIFICANCE: Biomechanical and architectural adaptation of the LVFW remains a central, yet overlooked, remodeling process post-MI. Our study indicates the biomechanical adaptation of the LVFW post-MI is highly individual and driven by altered fiber network architecture and collective changes in collagen fiber content, undulation, and stiffness. Our findings demonstrate the possibility of using cardiac strains to infer such fiber-level remodeling events through in-silico modeling, paving the way for in-vivo characterization of multiscale biomechanical indices in humans. Such indices will complement the traditional, organ-level measures of LV anatomy and function that often fall short of early prognostication of heart failure in MI.

Improved right ventricular strain estimation in rats using anisotropic diffusion filtering.

Calculating cardiac strains through speckle tracking echocardiography (STE) has shown promise as prognostic markers linked to functional indices and disease outcomes. However, the presence of acoustic shadowing often challenges the accuracy of STE in small animals such as rodents. The shadowing arises due to the complex anatomy of rodents, with operator dexterity playing a significant role in image quality. The effects of the semi-transparent shadows are further exacerbated in right ventricular (RV) imaging due to the thinness and rapid motion of the RV free wall (RVFW). The movement of the RVFW across the shadows distorts speckle tracking and produces unnatural and non-physical strains. The objective of this study was to minimize the effects of shadowing on STE by distinguishing "out-of-shadow" motion and identifying speckles in and out of shadow. Parasternal 2D echocardiography was performed, and short-axis B-mode (SA) images of the RVFW were acquired for a rodent model of pulmonary hypertension (n = 1). Following image acquisition, a denoising algorithm using edge-enhancing anisotropic diffusion (EED) was implemented, and the ensuing effects on strain analysis were visualized using a custom STE pipeline. Speckles in the shadowed regions were identified through a correlation between the filtered image and the original acquisition. Thus, pixel movement across the boundary was identified by enhancing the distinction between the shadows and the cardiac wall, and non-physical strains were suppressed. The strains obtained through STE showed expected patterns with enhanced circumferential contractions in the central region of the RVFW in contrast to smaller and nearly uniform strains derived from the unprocessed images.

Volumetric versus distortional deformation in rat lungs.

There are several lung diseases that lead to alterations in regional lung mechanics, including acute respiratory distress syndrome. Such alterations can lead to localized underventilation of the affected areas resulting in the overdistension of the surrounding healthy regions. They can also lead to the surrounding alveoli expanding unevenly or distorting. Therefore, the quantification of the regional deformation in the lungs offers insights into identifying the regions at risk of lung injury. Although few recent studies have developed image processing techniques to quantify the regional volumetric deformation in the lung from dynamic imaging, the presence and extent of distortional deformation in the lung, and its correlation with volumetric deformation, remain poorly understood. In this study, we present a method that uses the four-dimensional displacement field obtained from image registration to quantify both regional volumetric and distortional deformation in the lung. We used dynamic computed tomography scans in a healthy rat over the course of one respiratory cycle in free breathing. Non-rigid image registration was performed to quantify voxel displacement during respiration. The deformation gradient was calculated using the displacement field and its determinant was used to quantify regional volumetric deformation. Regional distortion was calculated as the ratio of maximum to minimum principal stretches using the isochoric part of the Cauchy green tensor. We found an inverse correlation between volumetric strains and distortion indicating that poorly expanding alveoli tend to experience larger distortion. The combination of regional volumetric strains and distortion may serve as high-fidelity biomarkers to identify the regions at risk of most adverse lung injuries.

A Micro-anatomical Model of the Infarcted Left Ventricle Border Zone to Study the Influence of Collagen Undulation.

Myocardial infarction (MI) results in cardiac myocyte death and often initiates the formation of a fibrotic scar in the myocardium surrounded by a border zone. Myocyte loss and collagen-rich scar tissue heavily influence the biomechanical behavior of the myocardium which could lead to various cardiac diseases such as systolic heart failure and arrhythmias. Knowledge of how myocyte and collagen micro-architecture changes affect the passive mechanical behavior of the border zone remains limited. Computational modeling provides us with an invaluable tool to identify and study the mechanisms driving the biomechanical remodeling of the myocardium post-MI. We utilized a rodent model of MI and an image-based approach to characterize the three-dimensional (3-D) myocyte and collagen micro-architecture at various timepoints post-MI. Left ventricular free wall (LVFW) samples were obtained from infarcted hearts at 1-week and 4-week post-MI (n = 1 each). Samples were labeled using immunoassays to identify the extracellular matrix (ECM) and myocytes. 3-D reconstructions of the infarct border zone were developed from confocal imaging and meshed to develop high-fidelity micro-anatomically accurate finite element models. We performed a parametric study using these models to investigate the influence of collagen undulation on the passive micromechanical behavior of the myocardium under a diastolic load. Our results suggest that although parametric increases in collagen undulation elevate the strain amount experienced by the ECM in both early- and late-stage MI, the sensitivity of myocytes to such increases is reduced from early to late-stage MI. Our 3-D micro-anatomical modeling holds promise in identifying mechanisms of border zone maladaptation post-MI.

Multiscale characterization of left ventricle active behavior in the mouse.

The myocardium possesses an intricately designed microarchitecture to produce an optimal cardiac contraction. The contractile behavior of the heart is generated at the sarcomere level and travels across several length scales to manifest as the systolic function at the organ level. While passive myocardial behavior has been studied extensively, the translation of active tension produced at the fiber level to the organ-level function is not well understood. Alterations in cardiac systolic function are often key sequelae in structural heart diseases, such as myocardial infarction and systolic heart failure; thus, characterization of the contractile behavior of the heart across multiple length scales is essential to improve our understanding of mechanisms collectively leading to depressed systolic function. In this study, we present a methodology to characterize the active behavior of left ventricle free wall (LVFW) myocardial tissues in mice. Combined with active tests in papillary muscle fibers and conventional in vivo contractility measurement at the organ level in an animal-specific manner, we establish a multiscale active characterization of the heart from fiber to organ. In addition, we quantified myocardial architecture from histology to shed light on the directionality of the contractility at the tissue level. The LVFW tissue activation-relaxation behavior under isometric conditions was qualitatively similar to that of the papillary muscle fiber bundle. However, the maximum stress developed in the LVFW tissue was an order of magnitude lower than that developed by a fiber bundle, and the time taken for active forces to plateau was 2-3 orders of magnitude longer. Although the LVFW tissue exhibited a slightly stiffer passive response in the circumferential direction, the tissues produced significantly larger active stresses in the longitudinal direction during active testing. Also, contrary to passive viscoelastic stress relaxation, active stresses relaxed faster in the direction with larger peak stresses. The multiscale experimental pipeline presented in this work is expected to provide crucial insight into the contractile adaptation mechanisms of the heart with impaired systolic function. STATEMENT OF SIGNIFICANCE: Heart failure cause significant alterations to the contractile-relaxation behavior of the yocardium. Multiscale characterization of the contractile behavior of the myocardium is essential to advance our understanding of how contractility translates from fiber to organ and to identify the multiscale mechanisms leading to impaired cardiac function. While passive myocardial behavior has been studied extensively, the investigation of tissue-level contractile behavior remains critically scarce in the literature. To the best of our knowledge, our study here is the first to investigate the contractile behavior of the left ventricle at multiple length scales in small animals. Our results indicate that the active myocardial wall is a function of transmural depth and relaxes faster in the direction with larger peak stresses.

Contractile Adaptation of the Left Ventricle Post-myocardial Infarction: Predictions by Rodent-Specific Computational Modeling.

Myocardial infarction (MI) results in cardiac myocyte death and the formation of a fibrotic scar in the left ventricular free wall (LVFW). Following an acute MI, LVFW remodeling takes place consisting of several alterations in the structure and properties of cellular and extracellular components with a heterogeneous pattern across the LVFW. The normal function of the heart is strongly influenced by the passive and active biomechanical behavior of the LVFW, and progressive myocardial structural remodeling can have a detrimental effect on both diastolic and systolic functions of the LV leading to heart failure. Despite important advances in understanding LVFW passive remodeling in the setting of MI, heterogeneous remodeling in the LVFW active properties and its relationship to organ-level LV function remain understudied. To address these gaps, we developed high-fidelity finite-element (FE) rodent computational cardiac models (RCCMs) of MI using extensive datasets from MI rat hearts representing the heart remodeling from one-week (1-wk) to four-week (4-wk) post-MI timepoints. The rat-specific models (n = 2 for each timepoint) integrate detailed imaging data of the heart geometry, myocardial fiber architecture, and infarct zone determined using late gadolinium enhancement prior to terminal measurements. The computational models predicted a significantly higher level of active tension in remote myocardium in early post-MI hearts (1-wk post-MI) followed by a return to near the control level in late-stage MI (3- and 4-wk post-MI). The late-stage MI rats showed smaller myofiber ranges in the remote region and in-silico experiments using RCCMs suggested that the smaller fiber helicity is consistent with lower contractile forces needed to meet the measured ejection fractions in late-stage MI. In contrast, in-silico experiments predicted that collagen fiber transmural orientation in the infarct region has little influence on organ-level function. In addition, our MI RCCMs indicated that reduced and potentially positive circumferential strains in the infarct region at end-systole can be used to infer information about the time-varying properties of the infarct region. The detailed description of regional passive and active remodeling patterns can complement and enhance the traditional measures of LV anatomy and function that often lead to a gross and limited assessment of cardiac performance. The translation and implementation of our model in patient-specific organ-level simulations offer to advance the investigation of individualized prognosis and intervention for MI.

Computational lung modelling in respiratory medicine.

Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure-function relationship in the lung.

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.

A machine learning model to estimate myocardial stiffness from EDPVR.

In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters [Formula: see text] and [Formula: see text] associated with fiber direction ([Formula: see text] and [Formula: see text]). After conducting permutation feature importance analysis, the ML performance further improved for [Formula: see text] ([Formula: see text]), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases.

A reduced-order model of the spine to study pediatric scoliosis.

The S-shaped curvature of the spine has been hypothesized as the underlying mechanical cause of adolescent idiopathic scoliosis. In earlier work, we proposed a reduced-order model in which the spine was viewed as an S-shaped elastic rod under torsion and bending. Here, we simulate the deformation of S-shaped rods of a wide range of curvatures and inflection points under a fixed mechanical loading. Our analysis determines three distinct axial projection patterns of these S-shaped rods: two loop (in opposite directions) patterns and one Lemniscate pattern. We further identify the curve characteristics associated with each deformation pattern, showing that for rods deforming in a Loop1 shape the position of the inflection point is the highest and the curvature of the rod is smaller compared to the other two types. For rods deforming in the Loop2 shape, the position of the inflection point is the lowest (closer to the fixed base) and the curvatures are higher than the other two types. These patterns matched the common clinically observed scoliotic curves-Lenke 1 and Lenke 5. Our S-shaped elastic rod model generates deformations that are similar to those of a pediatric spine with the same sagittal curvature characteristics and it can differentiate between the clinically observed deformation patterns.

A Semi-Analytic Elastic Rod Model of Pediatric Spinal Deformity.

The mechanism of the scoliotic curve development in healthy adolescents remains unknown in the field of orthopedic surgery. Variations in the sagittal curvature of the spine are believed to be a leading cause of scoliosis in this patient population. Here, we formulate the mechanics of S-shaped slender elastic rods as a model for pediatric spine under physiological loading. Second, applying inverse mechanics to clinical data of the subtypes of scoliotic spines, with characteristic 3D deformity, we determine the undeformed geometry of the spine before the induction of scoliosis. Our result successfully reproduces the clinical data of the deformed spine under varying loads, confirming that the prescoliotic sagittal curvature of the spine impacts the 3D loading that leads to scoliosis.