Utilization of an Artery-on-a-Chip to Unravel Novel Regulators and Therapeutic Targets in Vascular Diseases.

In this study, organ-on-chip technology is used to develop an in vitro model of medium-to-large size arteries, the artery-on-a-chip (AoC), with the objective to recapitulate the structure of the arterial wall and the relevant hemodynamic forces affecting luminal cells. AoCs exposed either to in vivo-like shear stress values or kept in static conditions are assessed to generate a panel of novel genes modulated by shear stress. Considering the crucial role played by shear stress alterations in carotid arteries affected by atherosclerosis (CAD) and abdominal aortic aneurysms (AAA) disease development/progression, a patient cohort of hemodynamically relevant specimens is utilized, consisting of diseased and non-diseased (internal control) vessel regions from the same patient. Genes activated by shear stress follow the same expression pattern in non-diseased segments of human vessels. Single cell RNA sequencing (scRNA-seq) enables to discriminate the unique cell subpopulations between non-diseased and diseased vessel portions, revealing an enrichment of flow activated genes in structural cells originating from non-diseased specimens. Furthermore, the AoC served as a platform for drug-testing. It reproduced the effects of a therapeutic agent (lenvatinib) previously used in preclinical AAA studies, therefore extending the understanding of its therapeutic effect through a multicellular structure.

Effect of ACE inhibition on glomerular permselectivity and tubular albumin concentration in the renal ablation model.

Despite the central role of tubular plasma proteins that characterize progressive kidney diseases, protein concentrations along the nephron in pathological conditions have not been quantified so far. We combined experimental techniques and theoretical analysis to estimate glomerular and tubular levels of albumin in the experimental model of 5/6 nephrectomy (Nx) in the rat, with or without angiotensin-converting enzyme (ACE) inhibition. We measured glomerular permselectivity by clearance of fluorescent Ficoll and albumin and used theoretical analysis to estimate tubular albumin. As expected, 5/6 Nx induced an elevation of the fractional clearance of the largest Ficoll molecules (radii >56 Å, P < 0.05), increasing the importance of the shunt pathway of the glomerular membrane and the albumin excretion rate (119 ± 41 vs. 0.6 ± 0.2 mg/24 h, P < 0.01). ACE inhibition normalized glomerular permselectivity and urinary albumin (0.5 ± 0.3 mg/24 h). Theoretical analysis indicates that with 5/6 Nx, an increased albumin filtration overcomes proximal tubule reabsorption, with a massive increase in average albumin concentration along the tubule, reaching the highest value of >2,500 µg/ml at the end of the collecting duct. ACE inhibition improved glomerular permselectivity, limiting albumin filtration under proximal tubule reabsorption capacity, with low albumin concentration along the entire nephron, averaging <13 µg/ml at the end of the collecting duct. These results reinforce our understanding of the mechanisms of renal disease progression and the effects of angiotensin II antagonism. They also suggest that evaluation of tubular protein concentration levels could help to identify patients at risk of kidney disease progression and to improve clinical management.

Modeling hemodynamics in intracranial aneurysms: Comparing accuracy of CFD solvers based on finite element and finite volume schemes.

Image-based computational fluid dynamics (CFD) has shown potential to aid in the clinical management of intracranial aneurysms, but its adoption in the clinical practice has been missing, partially because of lack of accuracy assessment and sensitivity analysis. To numerically solve the flow-governing equations, CFD solvers generally rely on 2 spatial discretization schemes: finite volume (FV) and finite element (FE). Since increasingly accurate numerical solutions are obtained by different means, accuracies and computational costs of FV and FE formulations cannot be compared directly. To this end, in this study, we benchmark 2 representative CFD solvers in simulating flow in a patient-specific intracranial aneurysm model: (1) ANSYS Fluent, a commercial FV-based solver, and (2) VMTKLab multidGetto, a discontinuous Galerkin (dG) FE-based solver. The FV solver's accuracy is improved by increasing the spatial mesh resolution (134k, 1.1m, 8.6m, and 68.5m tetrahedral element meshes). The dGFE solver accuracy is increased by increasing the degree of polynomials (first, second, third, and fourth degree) on the base 134k tetrahedral element mesh. Solutions from best FV and dGFE approximations are used as baseline for error quantification. On average, velocity errors for second-best approximations are approximately 1 cm/s for a [0,125] cm/s velocity magnitude field. Results show that high-order dGFE provides better accuracy per degree of freedom but worse accuracy per Jacobian nonzero entry as compared with FV. Cross-comparison of velocity errors demonstrates asymptotic convergence of both solvers to the same numerical solution. Nevertheless, the discrepancy between underresolved velocity fields suggests that mesh independence is reached following different paths.

pyNS: an open-source framework for 0D haemodynamic modelling.

A number of computational approaches have been proposed for the simulation of haemodynamics and vascular wall dynamics in complex vascular networks. Among them, 0D pulse wave propagation methods allow to efficiently model flow and pressure distributions and wall displacements throughout vascular networks at low computational costs. Although several techniques are documented in literature, the availability of open-source computational tools is still limited. We here present python Network Solver, a modular solver framework for 0D problems released under a BSD license as part of the archToolkit ( http://archtk.github.com ). As an application, we describe patient-specific models of the systemic circulation and detailed upper extremity for use in the prediction of maturation after surgical creation of vascular access for haemodialysis.

Computational model for simulation of vascular adaptation following vascular access surgery in haemodialysis patients.

An important number of surgical procedures for creation of vascular access (VA) in haemodialysis patients still results in non-adequate increase in blood flow (non-maturation). The rise in blood flow in arteriovenous shunts depends on vascular remodelling. Computational tools to predict the outcome of VA surgery would be important in this clinical context. The aim of our investigation was then to develop a 0D/1D computational model of arm vasculature able to simulate vessel wall remodelling and related changes in blood flow. We assumed that blood vessel remodelling is driven by peak wall shear stress. The model was calibrated with previously reported values of radial artery diameter and blood flow after end-to-end distal fistula creation. Good agreement was obtained between predicted changes in VA flow and in arterial diameter after surgery and corresponding measured values. The use of this computational model may allow accurate vascular surgery planning and ameliorate VA surgery outcomes.

An image-based modeling framework for patient-specific computational hemodynamics.

We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine.