Ex Vivo Regional Mechanical Characterization of Porcine Pulmonary Arteries

Application and reduction of a nonlinear hyperelastic wall model capturing ex vivo relationships between fluid pressure, area, and wall thickness in normal and hypertensive murine left pulmonary arteries

Pulmonary hypertension is a cardiovascular disorder manifested by elevated mean arterial blood pressure (>20 mmHg) together with vessel wall stiffening and thickening due to alterations in collagen, elastin, and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study introduces a novel approach to formulating a reduced order nonlinear elastic structural wall model for a large pulmonary artery. The model relating blood pressure and area is calibrated using ex vivo measurements of vessel diameter and wall thickness changes, under controlled pressure conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, and anisotropic model incorporating residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation, subset selection, and physical plausibility arguments are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced one layer model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive animals. The pressure-area relation developed in this novel manner has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.

Mechanical and matrix effects of short and long-duration exposure to beta-aminopropionitrile in elastase-induced model abdominal aortic aneurysm in mice

Objective

Evaluate the mechanical and matrix effects on abdominal aortic aneurysms (AAA) during the initial aortic dilation and after prolonged exposure to beta-aminopropionitrile (BAPN) in a topical elastase AAA model.

Methods

Abdominal aortae of C57/BL6 mice were exposed to topical elastase with or without BAPN in the drinking water starting 4 days before elastase exposure. For the standard AAA model, animals were harvested at 2 weeks after active elastase (STD2) or heat-inactivated elastase (SHAM2). For the enhanced elastase model, BAPN treatment continued for either 4 days (ENH2b) or until harvest (ENH2) at 2 weeks; BAPN was continued until harvest at 8 weeks in one group (ENH8). Each group underwent assessment of aortic diameter, mechanical testing (tangent modulus and ultimate tensile strength [UTS]), and quantification of insoluble elastin and bulk collagen in both the elastase exposed aorta as well as the descending thoracic aorta.

Results

BAPN treatment did not increase aortic dilation compared with the standard model after 2 weeks (ENH2, 1.65 ± 0.23 mm; ENH2b, 1.49 ± 0.39 mm; STD2, 1.67 ± 0.29 mm; and SHAM2, 0.73 ± 0.10 mm), but did result in increased dilation after 8 weeks (4.3 ± 2.0 mm; P = .005). After 2 weeks, compared with the standard model, continuous therapy with BAPN did not have an effect on UTS (24.84 ± 7.62 N/cm2; 18.05 ± 4.95 N/cm2), tangent modulus (32.60 ± 9.83 N/cm2; 26.13 ± 9.10 N/cm2), elastin (7.41 ± 2.43%; 7.37 ± 4.00%), or collagen (4.25 ± 0.79%; 5.86 ± 1.19%) content. The brief treatment, EHN2b, resulted in increased aortic collagen content compared with STD2 (7.55 ± 2.48%; P = .006) and an increase in UTS compared with ENH2 (35.18 ± 18.60 N/cm2; P = .03). The ENH8 group had the lowest tangent modulus (3.71 ± 3.10 N/cm2; P = .005) compared with all aortas harvested at 2 weeks and a lower UTS (2.18 ± 2.18 N/cm2) compared with both the STD2 (24.84 ± 7.62 N/cm2; P = .008) and ENH2b (35.18 ± 18.60 N/cm2; P = .001) groups. No differences in the mechanical properties or matrix protein concentrations were associated with abdominal elastase exposure or BAPN treatment for the thoracic aorta. The tangent modulus was higher in the STD2 group (32.60 ± 9.83 N/cm2; P = .0456) vs the SHAM2 group (17.99 ± 5.76 N/cm2), and the UTS was lower in the ENH2 group (18.05 ± 4.95 N/cm2; P = .0292) compared with the ENH2b group (35.18 ± 18.60 N/cm2). The ENH8 group had the lowest tangent modulus (3.71 ± 3.10 N/cm2; P = .005) compared with all aortas harvested at 2 weeks and a lower UTS (2.18 ± 2.18 N/cm2) compared with both the STD2 (24.84 ± 7.62 N/cm2; P = .008) and ENH2b (35.18 ± 18.60 N/cm2; P = .001) groups. Abdominal aortic elastin in the STD2 group (7.41 ± 2.43%; P = .035) was lower compared with the SHAM2 group (15.29 ± 7.66%). Aortic collagen was lower in the STD2 group (4.25 ± 0.79%; P = .007) compared with the SHAM2 group (12.44 ± 6.02%) and higher for the ENH2b (7.55 ± 2.48%; P = .006) compared with the STD2 group.

Conclusions

Enhancing an elastase AAA model with BAPN does not affect the initial (2-week) dilation phase substantially, either mechanically or by altering the matrix content. Late mechanical and matrix effects of prolonged BAPN treatment are limited to the elastase-exposed segment of the aorta.

Clinical Relevance

This paper explores the use of short- and long-term exposure to beta-aminopropionitrile to create an enhanced topical elastase abdominal aortic aneurysm model in mice. Readouts of aneurysm severity included loss of mechanical stability and vascular extracellular matrix composition reminiscent of what is seen in the course of human disease. Additionally, we show that the thoracic aorta, unlike the findings below the renal arteries, is not damaged in our animal model.

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.