The formation of distal anastomotic intimal hyperplasia (IH), one common mode of bypass graft failure, has been shown to occur in the areas of disturbed flow particular to this site. The nature of theflow in the segment of artery proximal to the distal anastomosis varies from case to case depending on the clinical situation presented. A partial stenosis of a bypassed arterial segment may allow residual prograde flow through the proximal artery entering the distal anastomosis of the graft. A complete stenosis may allow for zero flow in the proximal artery segment or retrograde flow due to the presence of small collateral vessels upstream. Although a number of investigations on the hemodynamics at the distal anastomosis of an end-to-side bypass graft have been conducted, there has not been a uniform treatment of the proximal artery flow condition. As a result, direct comparison of results from study to study may not be appropriate. The purpose of this work was to perform a three-dimensional computational investigation to study the effect of the proximal artery flow condition (i.e., prograde, zero, and retrograde flow) on the hemodynamics at the distal end-to-side anastomosis. We used the finite volume method to solve the full Navier-Stokes equations for steady flow through an idealized geometry of the distal anastomosis. We calculated the flow field and local wall shear stress (WSS) and WSS gradient (WSSG) everywhere in the domain. We also calculated the severity parameter (SP), a quantification of hemodynamic variation, at the anastomosis. Our model showed a marked difference in both the magnitude and spatial distribution of WSS and WSSG. For example, the maximum WSS magnitude on the floor of the artery proximal to the anastomosis for the prograde and zero flow cases is 1.8 and 3.9 dynes/cm2, respectively, while it is increased to 10.3 dynes/cm2 in the retrograde flow case. Similarly, the maximum value of WSSG magnitude on thefloor of the artery proximal to the anastomosis for the prograde flow case is 4.9 dynes/cm3, while it is increased to 13.6 and 24.2 dynes/cm3, respectively, in the zero and retrograde flow cases. The value of SP is highest for the retrograde flow case (13.7 dynes/cm3) and 8.1 and 12.1 percent lower than this for the prograde (12.6 dynes/cm3) and zero (12.0 dynes/cm3) flow cases, respectively. Our model results suggest that the flow condition in the proximal artery is an important determinant of the hemodynamics at the distal anastomosis of end-to-side vascular bypass grafts. Because hemodynamic forces affect the response of vascular endothelial cells, the flow situation in the proximal artery may affect IH formation and, therefore, long-term graft patency. Since surgeons have some control over the flow condition in the proximal artery, results from this study could help determine which flow condition is clinically optimal.
The intraluminal thrombus (ILT) commonly found within abdominal aortic aneurysm (AAA) may serve as a barrier to oxygen diffusion from the lumen to the inner layers of the aortic wall. The purpose of this work was to address this hypothesis and to assess the effects of AAA bulge diameter (dAAA) and ILT thickness (δ) on the oxygen flow. A hypothetical, three-dimensional, axisymmetric model of AAA containing ILT was created for computational analysis. Commercial software was utilized to estimate the volume flow of O2 per cell, which resulted in zero oxygen tension at the AAA wall. Solutions were generated by holding one of the two parameters fixed while varying the other. The supply of O2 to the AAA wall increases slightly and linearly with dAAA for a fixed δ. This slight increase is due to the enlarged area through which diffusion of O2 may take place. The supply of O2 was found to decrease quickly with increasing δ for a fixed dAAA due to the increased resistance to O2 transport by the ILT layer. The presence of even a thin, 3 mm ILT layer causes a diminished O2 supply (less than 4 × 10−10 μmol/min/cell). Normally functioning smooth muscle cells require a supply of 21 × 10−10 μmol/min/cell. Thus, our analysis serves to support our hypothesis that the presence of ILT alters the normal pattern of O2 supply to the AAA wall. This may lead to hypoxic cell dysfunction in the AAA wall, which may further lead to wall weakening and increased potential for rupture.
Modern regenerative medicine, and tissue engineering specifically, has benefited from a greater appreciation of the native extracellular matrix (ECM). Fibronectin, collagen, and elastin have entered the tissue engineer's toolkit; however, as fully decellularized biomaterials have come to the forefront in vascular engineering it has become apparent that the ECM is comprised of more than just fibronectin, collagen, and elastin, and that cell-instructive molecules known as matricellular proteins are critical for desired outcomes. In brief, matricellular proteins are ECM constituents that contrast with the canonical structural proteins of the ECM in that their primary role is to interact with the cell. Of late, matricellular genes have been linked to diseases including connective tissue disorders, cardiovascular disease, and cancer. Despite the range of biological activities, this class of biomolecules has not been actively used in the field of regenerative medicine. The intent of this review is to bring matricellular proteins into wider use in the context of vascular tissue engineering. Matricellular proteins orchestrate the formation of new collagen and elastin fibers that have proper mechanical properties-these will be essential components for a fully biological small diameter tissue engineered vascular graft (TEVG). Matricellular proteins also regulate the initiation of thrombosis via fibrin deposition and platelet activation, and the clearance of thrombus when it is no longer needed-proper regulation of thrombosis will be critical for maintaining patency of a TEVG after implantation. Matricellular proteins regulate the adhesion, migration, and proliferation of endothelial cells-all are biological functions that will be critical for formation of a thrombus-resistant endothelium within a TEVG. Lastly, matricellular proteins regulate the adhesion, migration, proliferation, and activation of smooth muscle cells-proper control of these biological activities will be critical for a TEVG that recellularizes and resists neointimal formation/stenosis. We review all of these functions for matricellular proteins here, in addition to reviewing the few studies that have been performed at the intersection of matricellular protein biology and vascular tissue engineering.
Ceramide and its metabolites have been implicated in numerous signaling pathways and can trigger cell type/stimulus-specific proliferation, differentiation, growth arrest, or apoptosis. We report that modulation of ceramide and its metabolites also occurs during the response of bovine aortic endothelial cells (bAECs) to mechanical strain, suggesting that ceramide signaling may affect the maintenance of a viable vascular endothelium during disease, vein grafting, and tissue engineering applications. Briefly, bAECs were exposed to 3%, 6%, 10%, or 12% cyclic strain at 1 Hz for up to 72 hrs and ceramide levels were investigated by mass spectrometry (MS). Additionally, ceramide levels were examined following cessation of 10% cyclic strain at 1 Hz for 24 hrs. MS analysis demonstrates that ceramide levels are elevated in response to cyclic mechanical strain, especially at or above 10% strain intensity. Our results suggest that ceramide regulation is fine-tuned to 6% strain. Following cessation of strain, ceramide levels quickly return to those of unstrained controls, suggesting that strain-related ceramide increases require continued application of strain. Using this knowledge of how ceramide is involved and regulated in response to mechanical stimulation, it may be possible to develop therapies to protect or enhance endothelial cells in tissue engineering and cell therapy applications. NIH 5 RO1 CA092389.
Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their risk of rupture - which is the 13<sup>th</sup> leading cause of death in the US - exceeds the risks associated with repair. Clinical intervention occurs when an aneurysm diameter exceeds 5.5 cm, but this "one-size fits all" criterion is insufficient, as it has been reported thatup to a quarter of AAA smaller than 5.5 cm do rupture. Therefore, there is a need for a more reliable, patient-specific, clinical tool to aide in the management of AAA. Biomechanical assessment of AAA is thought to provide critical physical insights to rupture risk, but clinical translataion of biomechanics-based tools has been limited due to the expertise, time, and computational requirements. It was estimated that through 2015, only 348 individual AAA cases have had biomechanical stress analysis performed, suggesting a deficient sample size to make such analysis relevant in the clinic. Artificial intelligence (AI) algorithms offer the potential to increase the throughput of AAA biomechanical analyses by reducing the overall time required to assess the wall stresses in these complex structures using traditional methods. This can be achieved by automatically segmenting regions of interest from medical images and using machine learning models to predict wall stresses of AAA. In this study, we present an automated AI-based methodology to predict the biomechanical wall stresses for individual AAA. The predictions using this approach were completed in a significantly less amount of time compared to a more traditional approach (~4 hours vs 20 seconds).
The objective of this study is to compare different types of meshes for the solution of static structural problems under large deformation conditions, using nonlinear materials. The three types of mesh used in this study are a structured hexahedral mesh, an unstructured tetrahedral mesh, and a hex-dominant mesh generated automatically by the bubble packing algorithm, [1]. The two geometries tested were a hypothetical, partially symmetric model of an Abdominal Aortic Aneurysm (AAA), and a three dimensional representation of an in vivo AAA reconstructed from CT scan images (Fig 1). In order to evaluate the accuracy of the finite element approximation the mean square (or L2) norm of the error was estimated.
