Consistent trilayer biomechanical modeling of aortic valve leaflet tissue

Strategies for Development of Synthetic Heart Valve Tissue Engineering Scaffolds

The current clinical solutions, including mechanical and bioprosthetic valves for valvular heart diseases, are plagued by coagulation, calcification, nondurability, and the inability to grow with patients. The tissue engineering approach attempts to resolve these shortcomings by producing heart valve scaffolds that may deliver patients a life-long solution. Heart valve scaffolds serve as a three-dimensional support structure made of biocompatible materials that provide adequate porosity for cell infiltration, and nutrient and waste transport, sponsor cell adhesion, proliferation, and differentiation, and allow for extracellular matrix production that together contributes to the generation of functional neotissue. The foundation of successful heart valve tissue engineering is replicating native heart valve architecture, mechanics, and cellular attributes through appropriate biomaterials and scaffold designs. This article reviews biomaterials, the fabrication of heart valve scaffolds, and their in-vitro and in-vivo evaluations applied for heart valve tissue engineering.

An integrated finite element method and machine learning algorithm for brain morphology prediction

The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.

A Coupled Multiscale Approach to Modeling Aortic Valve Mechanics in Health and Disease

Mechano-biological processes in the aortic valve span multiple length scales ranging from the molecular and cell to tissue and organ levels. The valvular interstitial cells residing within the valve cusps sense and actively respond to leaflet tissue deformations caused by the valve opening and closing during the cardiac cycle. Abnormalities in these biomechanical processes are believed to impact the matrix-maintenance function of the valvular interstitial cells, thereby initiating valvular disease processes such as calcific aortic stenosis. Understanding the mechanical behavior of valvular interstitial cells in maintaining tissue homeostasis in response to leaflet tissue deformation is therefore key to understanding the function of the aortic valve in health and disease. In this study, we applied a multiscale computational homogenization technique (also known as “FE2”) to aortic valve leaflet tissue to study the three-dimensional mechanical behavior of the valvular interstitial cells in response to organ-scale mechanical loading. We further considered calcific aortic stenosis with the aim of understanding the likely relationship between the valvular interstitial cell deformations and calcification. We find that the presence of calcified nodules leads to an increased strain profile that drives further growth of calcification.

Subject-specific multiscale modeling of aortic valve biomechanics

A Finite Element workflow for the multiscale analysis of the aortic valve biomechanics was developed and applied to three physiological anatomies with the aim of describing the aortic valve interstitial cells biomechanical milieu in physiological conditions, capturing the effect of subject-specific and leaflet-specific anatomical features from the organ down to the cell scale. A mixed approach was used to transfer organ-scale information down to the cell-scale. Displacement data from the organ model were used to impose kinematic boundary conditions to the tissue model, while stress data from the latter were used to impose loading boundary conditions to the cell level. Peak of radial leaflet strains was correlated with leaflet extent variability at the organ scale, while circumferential leaflet strains varied over a narrow range of values regardless of leaflet extent. The dependency of leaflet biomechanics on the leaflet-specific anatomy observed at the organ length-scale is reflected, and to some extent emphasized, into the results obtained at the lower length-scales. At the tissue length-scale, the peak diastolic circumferential and radial stresses computed in the fibrosa correlated with the leaflet surface area. At the cell length-scale, the difference between the strains in two main directions, and between the respective relationships with the specific leaflet anatomy, was even more evident; cell strains in the radial direction varied over a relatively wide range (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.36-0.87$$\end{document}0.36-0.87) with a strong correlation with the organ length-scale radial strain (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}= 0.95$$\end{document}R2=0.95); conversely, circumferential cell strains spanned a very narrow range (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.75-0.88$$\end{document}0.75-0.88) showing no correlation with the circumferential strain at the organ level (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{2}= 0.02$$\end{document}R2=0.02). Within the proposed simulation framework, being able to account for the actual anatomical features of the aortic valve leaflets allowed to gain insight into their effect on the structural mechanics of the leaflets at all length-scales, down to the cell scale.

Splitting dynamics of ferrofluid droplets inside a microfluidic T-junction using a pulse-width modulated magnetic field in micro-magnetofluidics

Micro-magnetofluidics offers a promising tool for better control over the ferrofluid droplet manipulation which has been vastly utilized in biomedical applications in recent years. In this study, the ferrofluid droplet splitting under an asymmetric Pulse-Width-Modulated (PWM) magnetic field in a T-junction is numerically investigated using a finite volume method and VOF two-phase model. By utilizing the PWM magnetic field, two novel regimes of ferrofluid droplet splitting named as Flowing through the Same Branch (FSB) and Double Splitting (DS) have been observed for the first time. In the FSB regime, the daughter droplets move out of the same microchannel outlet, and in the DS regime, the droplet splitting occurs two times which results in generating three daughter droplets. The main problem related to the asymmetric droplet splitting under a steady magnetic field is daughter droplet trapping. By using a PWM magnetic field, this issue is resolved and the trapped/escaped regions are obtained in terms of the duty cycle and dimensionless magnetic field frequency. The effects of six important dimensionless parameters on the splitting ratio, including magnetic Bond number, duty cycle, dimensionless magnetic field frequency, capillary number, dimensionless mother droplet length, and dimensionless dipole position are investigated. The results showed that the splitting ratio increases with increasing magnetic Bond number or duty cycle, or decreasing the dimensionless magnetic field frequency. Eventually, a correlation is offered for the splitting ratio based on the dimensionless variables with an average relative error of 2.67%.

Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of over 12,000 finite element simulations of mechanical behaviour and stress distribution in these arterial models representative of atherosclerotic plaques. We formulate the training data to predict the maximum von Mises stress which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of attributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method which has far-reaching implications for real-time and multi-scale prediction tasks in biomechanics.

After 50 Years of Heart Transplants: What Does the Next 50 Years Hold for Cardiovascular Medicine? A Perspective From the International Society for Applied Cardiovascular Biology

The first successful heart transplant 50 years ago by Dr.Christiaan Barnard in Cape Town, South Africa revolutionized cardiovascular medicine and research. Following this procedure, numerous other advances have reduced many contributors to cardiovascular morbidity and mortality; yet, cardiovascular disease remains the leading cause of death globally. Various unmet needs in cardiovascular medicine affect developing and underserved communities, where access to state-of-the-art advances remain out of reach. Addressing the remaining challenges in cardiovascular medicine in both developed and developing nations will require collaborative efforts from basic science researchers, engineers, industry, and clinicians. In this perspective, we discuss the advancements made in cardiovascular medicine since Dr. Barnard's groundbreaking procedure and ongoing research efforts to address these medical issues. Particular focus is given to the mission of the International Society for Applied Cardiovascular Biology (ISACB), which was founded in Cape Town during the 20th celebration of the first heart transplant in order to promote collaborative and translational research in the field of cardiovascular medicine.

A New Growth Model for Aortic Valve Calcification

Calcific aortic valve disease (CAVD) is a progressive disease in which minerals accumulate in the tissue of the aortic valve cusps, stiffening them and preventing valve opening and closing. The process of valve calcification was found to be similar to that of bone formation including cell differentiation to osteoblast-like cells. Studies have shown the contribution of high strains to calcification initiation and growth process acceleration. In this paper, a new strain-based calcification growth model is proposed. The model aims to explain the unique shape of the calcification and other disease characteristics. The calcification process was divided into two stages: Calcification initiation and calcification growth. The initiation locations were based on previously published findings and a reverse calcification technique (RCT), which uses computed tomography (CT) scans of patients to reveal the calcification initiation point. The calcification growth process was simulated by a finite element model of one aortic valve cusp loaded with cyclic loading. Similar to Wolff's law, describing bone response to stress, our model uses strains to drive calcification formation. The simulation grows calcification from its initiation point to its full typical stenotic shape. Study results showed that the model was able to reproduce the typical calcification growth pattern and shape, suggesting that strain is the main driving force behind calcification progression. The simulation also sheds light on other disease characteristics, such as calcification growth acceleration as the disease progresses, as well as sensitivity to hypertension.