Marcos Latorre’s research while affiliated with Polytechnic University of Valencia and other places

Equilibrated fluid-solid-growth (FSGe) is a fast, open source, three-dimensional (3D) computational platform for simulating interactions between instantaneous hemodynamics and long-term vessel wall adaptation through growth and remodeling (G&R). Such models are crucial for capturing adaptations in health and disease and following clinical interventions. In traditional G&R models, this feedback is modeled through highly simplified fluid models, neglecting local variations in blood pressure and wall shear stress (WSS). FSGe overcomes these inherent limitations by strongly coupling the 3D Navier-Stokes equations for blood flow with a 3D equilibrated constrained mixture model (CMMe) for vascular tissue G&R. CMMe allows one to predict long-term evolved mechanobiological equilibria from an original homeostatic state at a computational cost equivalent to that of a standard hyperelastic material model. In illustrative computational examples, we focus on the development of a stable aortic aneurysm in a mouse model to highlight key differences in growth patterns and fluid-solid feedback between FSGe and solid-only G&R models. We show that FSGe is especially important in blood vessels with asymmetric stimuli. Simulation results reveal greater local variation in fluid-derived WSS than in intramural stress (IMS). Thus, differences between FSGe and G&R models became more pronounced with the growing influence of WSS relative to pressure. Future applications in highly localized disease processes, such as for lesion formation in atherosclerosis, can now include spatial and temporal variations of WSS.

Thoracic aortic aneurysms are characterized by a progressive loss of biomechanical functionality resulting from degenerative changes in wall composition, microstructure, and mechanical properties. Among the many causes of these lesions, Marfan syndrome is the most common heritable condition, resulting from mutations to the gene that codes the elastin-associated glycoprotein fibrillin-1. Key histopathological features of the aneurysmal Marfan aorta include extensive degradation of elastic fibres, significant remodelling of fibrillar collagens and compromised smooth muscle cell function. Computational growth and remodelling models have confirmed the importance of compromised elastic fibre integrity in aneurysmal dilatation, but we show here that this contributor alone is not sufficient to describe biomechanical data collected from the two most common mouse models of Marfan syndrome. Rather, our simulations suggest that compromised mechanosensing and mechanoregulation of extracellular matrix by mural cells also play central roles in the natural history. Determination of optimal disease-contributing parameters further suggests a rapid reduction in cellular mechanosensing and mechanoregulation relative to diminished elastic fibre integrity, highlighting the importance of inter- and intra-lamellar elastin in the Marfan aorta. Aneurysmal dilatation in Marfan syndrome thus results from multiple contributors to progressive degeneration of the aortic wall, and computational mechanobiological models can help disentangle these contributions.

We implement full, three-dimensional constrained mixture theory for vascular growth and remodeling into a finite element fluid-structure interaction (FSI) solver. The resulting "fluid-solid-growth" (FSG) solver allows long term, patient-specific predictions of changing hemodynamics, vessel wall morphology, tissue composition, and material properties. This extension from short term (FSI) to long term (FSG) simulations increases clinical relevance by enabling mechanobioloigcally-dependent studies of disease progression in complex domains.

Blood vessels grow and remodel in response to mechanical stimuli. Many computational models capture this process phenomenologically, by assuming stress homeostasis, but this approach cannot unravel the underlying cellular mechanisms. Mechano-sensitive Notch signaling is well-known to be key in vascular development and homeostasis. Here, we present a multiscale framework coupling a constrained mixture model, capturing the mechanics and turnover of arterial constituents, to a cell–cell signaling model, describing Notch signaling dynamics among vascular smooth muscle cells (SMCs) as influenced by mechanical stimuli. Tissue turnover was regulated by both Notch activity, informed by in vitro data, and a phenomenological contribution, accounting for mechanisms other than Notch. This novel framework predicted changes in wall thickness and arterial composition in response to hypertension similar to previous in vivo data. The simulations suggested that Notch contributes to arterial growth in hypertension mainly by promoting SMC proliferation, while other mechanisms are needed to fully capture remodeling. The results also indicated that interventions to Notch, such as external Jagged ligands, can alter both the geometry and composition of hypertensive vessels, especially in the short term. Overall, our model enables a deeper analysis of the role of Notch and Notch interventions in arterial growth and remodeling and could be adopted to investigate therapeutic strategies and optimize vascular regeneration protocols.

Pregnancy associates with dramatic changes in maternal cardiovascular physiology that ensure that the utero-placental circulation can support the developing fetus. Particularly striking is the marked flow-induced remodeling of uterine arteries during pregnancy and their recovery following birth. Whereas details are available in the literature on alterations in hemodynamics within and changes in the dimensions of uterine arteries during and following pregnancy in mice, we report here the first biaxial biomechanical phenotyping of these arteries during this dynamic period of growth and remodeling (G&R). To gain additional insight into the measured G&R, we also use a computational constrained mixture model to describe and predict findings, including simulations related to complications that may arise during pregnancy. It is found that dramatic pregnancy-induced remodeling of the uterine artery is largely, but not completely, reversed in the postpartum period, which appears to be driven by increases in collagen turnover among other intramural changes. By contrast, data on the remodeling of the ascending aorta, an elastic artery, reveal modest changes that are fully recovered postpartum. There is strong motivation to continue biomechanical studies on this critical aspect of women’s health, which has heretofore not received appropriate consideration from the biomechanics community.

Background. Marfan Syndrome is a primary cause of thoracic aortic aneurysms; it arises from dysfunctional fibrillin-1, which normally stabilizes elastic fibers and promotes smooth muscle mechano-sensing of the matrix. Despite significant advancements, clear correlations between microstructural integrity and aortic functionality remain wanting. Methods. Age-matched wild-type, Fbn1C1041G/+[1], and Fbn1mgR/mgR[2] mice represented three stages of disease severity. Experiments quantified specimen-specific thoracic aortopathy in terms of: (1) mechanical metrics from ex vivo biaxial testing that were described by a four-fiber family hyperelastic model[3]; (2) microstructural metrics[4] from ex vivo multiphoton microscopy including elastin porosity, density, and engagement of collagen fibers and cells; (3) cardiac function from in vivo ultrasound and µCT imaging. Material properties were incorporated within a mechanobiologically equilibrated constrained mixture model of arterial growth and remodeling (G&R)[5]. The analysis assessed long-term impacts of locally compromised elastin integrity, cellular mechanosensing and mechanoregulation, collagen turnover, and endothelial function on disease progression through perturbations to the initial homeostatic state. Results. Aortic dilatation correlated strongly with key mechanical metrics of compromised aortic functionality as well as with elastin defects, collagen remodeling, and altered cellular function. Variable dilatations at a given age reflected a "pseudo-time" of progressive deterioration consistent with a progressive anoikis. The G&R model reproduces the same trends in aortic dilatation, stored energy, and circumferential stiffness with increasing losses of elastic fiber integrity. The progressive deterioration of elastic fibers and mechano-sensing appear to be primary drivers of aberrant tissue remodeling and associated dilatation in the Marfan aorta, which is characterized by progressive stiffening.

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta that can lead to life-threatening dissection or rupture. In vivo assessments of TAA progression are largely limited to measurements of aneurysm size and growth rate. There is promise, however, that computational modelling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating mechanobiological insults. We present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify TAA contributing factors using synthetic finite-element-based datasets. For training, we employ a constrained mixture model of aortic growth and remodelling to generate maps of local aortic dilatation and distensibility for multiple TAA risk factors. We evaluate the performance of the surrogate model for insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We propose two frameworks, one trained on sparse information and one on full-field greyscale images, to gain insight into a preferred neural operator-based approach. We show that this continuous learning approach can predict the patient-specific insult profile associated with any given dilatation and distensibility map with high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of applying DeepONet to support transfer learning of patient-specific inputs to predict TAA progression.

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Several genetic mutations and predisposing factors that contribute to TAA have been studied in mouse models to characterize specific changes in aortic microstructure and material properties that result from a wide range of mechanobiological insults. Assessments of TAA progression in vivo is largely limited to measurements of aneurysm size and growth rate. It has been shown that aortic geometry alone is not sufficient to predict the patient-specific progression of TAA but computational modeling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating insults. In this work, we present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify contributing factors for TAA by using FE-based datasets of aortic growth and remodeling resulting from prescribed insults. For training data, we investigate multiple types of TAA risk factors and spatial distributions within a constrained mixture model to generate axial--azimuthal maps of aortic dilatation and distensibility. The trained network is then capable of predicting the initial distribution and extent of the insult from a given set of dilatation and distensibility information. Two DeepONet frameworks are proposed, one trained on sparse information and one on full-field grayscale images, to gain insight into a preferred neural operator-based approach. Performance of the surrogate models is evaluated through multiple simulations carried out on insult distributions varying from fusiform to complex. We show that the proposed approach can predict patient-specific mechanobiological insult profile with a high accuracy, particularly when based on full-field images.