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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: J Neurointerv Surg. 2019 Apr 12;11(10):999–1003. doi: 10.1136/neurintsurg-2018-014669

Genetic correlates of wall shear stress in a patient-specific 3D-printed cerebral aneurysm model

Michael R Levitt 1,2,3,4, christian Mandrycky 5, Ashley Abel 1, cory M Kelly 1,4, samuel Levy 1,4, Venkat K Chivukula 3, Ying Zheng 4,5, Alberto Aliseda 1,3,4, Louis J Kim 1,2,4
PMCID: PMC6744304  NIHMSID: NIHMS1023727  PMID: 30979845

Abstract

Objectives

To study the correlation between wall shear stress and endothelial cell expression in a patient-specific, three-dimensional (3D)-printed model of a cerebral aneurysm.

Materials and methods

A 3D-printed model of a cerebral aneurysm was created from a patient’s angiogram. After populating the model with human endothelial cells, it was exposed to media under flow for 24 hours. Endothelial cell morphology was characterized in five regions of the 3D-printed model using confocal microscopy. Endothelial cells were then harvested from distinct regions of the 3D-printed model for mRNA collection and gene analysis via quantitative polymerase chain reaction (qPCR.) Cell morphology and mRNA measurement were correlated with computational fluid dynamics simulations.

Results

The model was successfully populated with endothelial cells, which survived under flow for 24 hours. Endothelial morphology showed alignment with flow in the proximal and distal parent vessel and aneurysm neck, but disorganization in the aneurysm dome. Genetic analysis of endothelial mRNA expression in the aneurysm dome and distal parent vessel was compared with the proximal parent vessels. ADAMTS-1 and NOS3 were downregulated in the aneurysm dome, while GJA4 was upregulated in the distal parent vessel. Disorganized morphology and decreased ADAMTS-1 and NOS3 expression correlated with areas of substantially lower wall shear stress and wall shear stress gradient in computational fluid dynamics simulations.

Conclusions

Creating 3D-printed models of patient-specific cerebral aneurysms populated with human endothelial cells is feasible. Analysis of these cells after exposure to flow demonstrates differences in both cell morphology and genetic expression, which correlate with areas of differential hemodynamic stress.

INTRODUCTION

Work attempting to understand aneurysm formation, growth, rupture, and effects of treatment has hypothesized a connection between mechanical stresses on the aneurysmal wall, and endothelial dysfunction. These studies have frequently employed computational fluid dynamics (CFD) techniques to simulate the flow of blood inside intracranial arteries and aneurysms, calculating metrics from the hemodynamic environment of aneurysms before and after treatment.1 These CFD simulations have been successful in quantifying important hemodynamic factors such as wall shear stress (WSS) and WSS gradient (WSSG), both of which have been implicated in aneurysmal growth, rupture, and treatment outcome.2,3 Because endothelial cells contain mechanosensors that respond to changes in such hemodynamic stresses,4 these metrics have been studied in relation to their effects on endothelial dysfunction, abnormal vascular remodeling, and inflammation, which are fundamental components of cerebral aneurysm pathophysiology.3,5 For instance, VCAM-1 is an adhesion molecule upregulated in aneurysmal regions of low WSS,6,7 while the matrix metalloproteinase ADAMTS-1 is upregulated in areas of elevated WSS and WSSG.8,9

A major drawback of existing CFD studies in clinical applications (such as risk of aneurysm growth or rupture, or outcome of aneurysm treatment) is that they do not directly measure the pathological effects of such hemodynamic variables on biological tissue, since CFD uses computer models to evaluate blood flow and shear stress. These models influence, through poorly understood mechanisms, the vascular biology controlling aneurysmal evolution. Thus, the connection between aneurysmal hemodynamics observed in CFD studies and their pathobiological impact is inferred from a variety of sources, such as human studies of non-aneurysmal domains, animal studies, and highly simplified in vitro experiments, in which the response of endothelial cells to blood flow stresses is measured.3,6,8,10–14 The design of these experiments, such as studying the effects of laminar or disturbed flow on endothelial cells in a straight tube or idealized aneurysm shape at small scale,15 or ligating the carotid arteries of animals and measuring subsequent vascular endothelial remodeling,14 do not reflect the patient-specific, highly complex anatomy of human cerebral aneurysms and thus cannot provide sufficient quantification for predictive prognosis.

We have developed a patient-specific cerebral aneurysm model populated with human endothelial cells, and studied the endothelial cell response to hemodynamic stresses. Our method suggested that areas of pathological hemodynamic stresses in CFD simulations can be correlated with subsequent endothelial transcription and expression of important vascular factors in 3D-printed models, which might be used to quantify the relationship between hemodynamics and vascular biology.

MATERIALS AND METHODS

Creation of a 3D-printed model

A 3D rotational angiogram from a patient enrolled in a clinical study of cerebral aneurysm hemodynamics was used for this pilot study, as approved by the institutional review board (figure 1A). First, a ‘positive’ aneurysm model and the surrounding vessels were 3D printed in a stereolithography printer (Form 2, Formlabs, Somerville, Massachusetts, USA) using photoreactive resins (High Temp resin, Formlabs, printed at 25 μm layer height, figure 1B). The aneurysm model included the aneurysmal sac, and proximal and distal parent vessels, with precise anatomy and scale matching the patient’s 3D rotational angiogram. The ‘positive’ 3D-printed model was smoothed by sanding and polishing until transparent (Novus Plastic Polish, St Paul, Minnesota, USA). The model was embedded in polydimethylsiloxane (PDMS) using a customized acrylic housing to achieve thin walls for optimal imaging and endothelial cell sampling. After crosslinking the PDMS, the positive aneurysm model was removed from the PDMS structure to generate a negative aneurysm lumen (the final 3D-printed aneurysm model, figure 1C) for flow measurement and endothelialization. Any defects created during this process were corrected by resealing with PDMS to ensure a high burst pressure, and to prevent leakage under flow. This process was repeated to create identical models of the same cerebral aneurysm.

Figure 1.

Figure 1

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(A) A three-dimensional rotational angiogram of an aneurysm is obtained during treatment and used to create both a 3D-printed model and computational fluid dynamics simulation. (B) 3D-printed, patient-specific aneurysm model based on the angiogram in (A). (C) Fabricated polydimethylsiloxane aneurysm model with open lumens ready for cell seeding and perfusion.

All PDMS models were sterilized through a high-pressure and high-temperature autoclave cycle, followed by plasma treatment in a Plasma Prep II Etcher (SPI Supplies, West Chester, Pennsylvania, USA) for 1 min to make the PDMS surface hydrophilic. For gene expression experiments a 5 μg/mL fibronectin solution (MilliporeSigma, Burlington, Massachusetts, USA) was perfused immediately after plasma treatment and incubated for 1 hour at 37°C to enhance cell adhesion during seeding. For morphologic characterization a single PDMS model was plasma treated for 1 min and incubated with 0.2% gelatin for 20 min at 37°C. Gene expression models were seeded with a suspension of human carotid artery endothelial cells (HCtAEC; Cell Applications, San Diego, California, USA) at 1 million cells/mL and incubated at 37°C for 15 min. Models were inverted once while seeding to ensure even cell coverage, and fresh medium was used to remove cells that had not adhered. The morphology model was seeded using the same protocol, but with human umbilical vein endothelial cells (HUVEC; Lonza, Morristown, New Jersey, USA). After 12 hours of attachment, each endothelialized model was connected to a peristaltic pump (Cole-Parmer, Vernon Hills, Illinois, USA) to drive the flow of the medium at a rate of 150 mL/min for 24 hours with a standardized pulsatile waveform reaching a peak pressure of ~40 mm Hg.16 The flow medium was endothelial cell growth medium supplemented with 3.5% dextran (Sigma-Aldrich, St Louis, Missouri, USA) to mimic the dynamic viscosity of blood.17

CFD simulation

The same 3D rotational angiogram, used for the flow phantom model creation above, was used for CFD simulation, which has been previously described.18 Briefly, image segmentation was performed using the Vascular Modeling Toolkit (www.vmtk.org), creating a computerized 3D reconstruction of the vascular lumen and aneurysm. Then, tetrahedral meshes were generated by StarCCM + software (CD-adapco, Melville, New York, USA). CFD simulations were executed using ANSYS Fluent (ANSYS Inc, Canonsburg, Pennsylvania, USA), a finite-volume flow solver with ample reported use for the simulation of intracranial aneurysm hemodynamics. Very high spatial and temporal resolution was used to capture the hemodynamics in the intra-aneurysmal and perianeurysmal vasculature—CFD mesh sizes ranged from 50 to 100 μm, amounting to nearly 1 million elements, and a time step size of 0.0005 s was used for the simulations. We set the convergence criteria for the continuity and the momentum equations (Navier-Stokes equations) to 10-6. Each simulation was run for a minimum of three cardiac cycles, and the first two cycles were discarded to ensure that the simulations were independent of the initial conditions. Velocity stream-lines were created and hemodynamic variables (WSS and WSSG) were calculated over the entire third cardiac cycle to obtain the spatio-temporal variation of hemodynamic stimuli for the aneurysm and surrounding vasculature. Consistent with perfusion in the 3D-printed model, velocity inlet conditions were applied to both vertebral arteries.

Endothelial cell morphometric analysis

To qualitatively characterize endothelial cell morphology, one HUVEC seeded model (n=1) was fixed after 24 hours and washed with phosphate-buffered saline. Immunofluorescence staining was performed for junctional protein CD31. Images of this model were obtained through wide-field and fluorescent confocal microscopy to obtain large-scale z-stack images for five distinct aneurysm regions: the proximal parent vessel, the proximal aneurysm neck, the aneurysm dome, the distal aneurysm neck and the distal parent vessel (figure 2). These characteristics were qualitatively compared with hemodynamic variables WSS and WSSG in CFD simulations of the same aneurysm.

Figure 2.

Figure 2

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Human umbilical vein endothelial cells are cultured under pulsatile flow (arrow) at a rate of 150 μL/min for 24 hours in the aneurysm model created in figure 1. Zoomed-in confocal microscopy shows confluency and a patent monolayer. Cells show alignment in the proximal parent vessel (a), proximal aneurysm neck (b), distal aneurysm neck (d) and distal parent vessel (e) but not in the aneurysm dome (c). Note that flow was introduced from both inlets, as shown in figure 1.

Endothelial cell genetic analysis

In HCtAEC endothelialized 3D-printed aneurysm models (n=3), three zones were defined for endothelial cell analysis: (1) the proximal parent vessel, (2) the aneurysm dome, and (3) the distal parent vessel. Each of the zones was mechanically separated at the end of each experiment and independently perfused with RLT lysis buffer (Qiagen, Hilden, Germany), and the lysate was collected. Total RNA from each zone was purified using RNAeasy Mini Kit (Qiagen) and RT-PCR was performed using the real-time PCR system (Applied Biosystems, Foster City, California, USA) with Fast SYBR Green Master Mix (Applied Biosystems). The abundance of several key vascular factors (VCAM-1, ADAMTS-1, MMP-9, GJA4, and NOS3) was determined relative to an internal control using glyceraldehyde-3-phosphate dehydrogenase RNA. These factors were selected based on previous studies relating their expression to either hemodynamic stress or aneurysm pathophysiology.7-10,14,15,19–22 The three regions were compared with hemodynamics metrics from CFD simulations of the same aneurysmal anatomy to assess the effect of intra-aneurysmal WSS and WSSG on endothelial expression of each of the key vascular factors.

Statistical analysis

Cell morphology and qPCR data were evaluated using R and Minitab 18 (State College, Pennsylvania, USA). Data were analyzed in a repeated measures analysis of variance design and pairwise comparisons were performed using Tukey’s method. P values <0.05 were considered statistically significant.

RESULTS

All aneurysmal models were successfully populated with human endothelial cells, which survived flow for at least 24 hours and formed a robust monolayer in the majority of the lumen area (figure 2). Endothelial cells showed alignment to flow direction in the proximal and distal parent vessel, and in the proximal and distal aneurysm neck, but not in the aneurysm dome, where flow is not unidirectional. This difference in endothelial morphology also correlated with differences in hemodynamics between the aneurysm dome and the parent vessel: velocity, WSS, and WSSG were substantially lower in the aneurysm dome in those regions in which endothelial alignment was not consistent (figure 3).

Figure 3.

Figure 3

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Patient-specific computational fluid dynamics simulations of the aneurysm in figure 1, with endothelial cell regions a-e defined in figure 2. (A) Velocity streamlines. (B) Wall shear stress (WSS). (C) Wall shear stress gradient (WSSG). Note that the majority of the aneurysm dome experiences relatively low WSS and WSSG, especially compared with the impingement zone at the distal aneurysm neck and downstream parent vessel.

Endothelial cell harvest for genetic expression analysis yielded consistent total RNA levels between models in the proximal parent vessel (1.90±0.08 μg), aneurysm dome (1.07±0.07 μg), and distal parent vessel (1.54 ±. 01 μg) regions, sufficient for large-scale screening and qPCR studies of over 100 genes. Genetic analysis of five key vascular factors (ADAMTS-1, GJA4, MMP-9, NOS3, and VCAM-1) is shown in figure 4. Compared with the proximal parent vessel and the aneurysm dome, GJA4 was significantly lower in the distal parent vessel (p<0. 05). The expression of ADAMTS-1 and NOS3 suggested a lower concentration in the aneurysm dome when compared with both parent vessel regions in all three model runs, but this change was not statistically significant. Neither MMP-9 nor VCAM-1 showed a consistent trend of expression in the three vessel regions. Decreased levels of ADAMTS-1 and NOS3 correlated with areas of substantially low velocity, WSS, and WSSG in the aneurysm dome in CFD simulations (figure 3).

Figure 4.

Figure 4

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mRNA quantification (via quantitative polymerase chain reaction analysis) of human carotid artery endothelial cell genes in the proximal parent vessel (figure 2a), aneurysm region (figure 2c), and distal parent vessel (figure 2e). Note the significantly reduced expression of GJA4 in the distal vessel region, and trends towards reduced expression of ADAMTS-1 and NOS3 in the aneurysm dome region (figure 2c), corresponding to low velocity, wall shear stress, and wall shear stress gradient in computational fluid dynamics simulations (figure 3). *p<0.05.

DISCUSSION

We have demonstrated that creating and maintaining a monolayer of living human endothelial cells in a patient-specific, 3D-printed model of a cerebral aneurysm is feasible, and that exposure to fluid flow results in changes in both endothelial morphology and expression of key vascular factors. Both endothelial morphology and genetic expression can be qualitatively associated with hemodynamic stress metrics from CFD simulations, with differential expression of ADAMTS-1 and NOS3 related to predominantly low WSS in the aneurysm dome, and reduced GJA4 in the distal parent vessel. This study, while exploratory, provides proof of the concept that specific areas of differential hemodynamic stress characterized by CFD simulations of a cerebral aneurysm and the surrounding vasculature can be directly related to endothelial responses in an in vitro model of the same aneurysm, and that this relationship could subsequently be quantified.

Our work seeks to go beyond existing studies aimed at understanding the pathobiology of cerebral aneurysm endothelial cells. Animal models of the pathobiological effects of hemodynamic stress have provided a solid background in identifying key factors of endothelial dysfunction driving aneurysm formation, growth, and rupture as related to hemodynamic stresses, such as ADAMTS-1, VCAM-1, MCP-1, and PDGF-B.7,8,10,19,23 However, animal models of cerebral aneurysms require artificial aneurysm creation, the anatomic result of which is unlike patient-specific human cerebral aneurysm morphology. This hinders the animal model approach, as it is cannot relate specific patterns of hemodynamic stresses as they appear in clinically relevant CFD simulations with disease-related human endothelial expression.24

Direct study of human vascular endothelial responses to pathological levels of hemodynamic stress in cerebral aneurysms, on the other hand, has not been extensively or regularly performed in parallel with CFD studies of aneurysm hemodynamics, since in vivo studies of cerebral aneurysms observed during surgery have examined only gross external features, such as wall thinning25 or atherosclerosis,26,27 and related them to areas of low WSS on corresponding CFD simulations of patient anatomy.

Ex vivo study of aneurysm tissue, while mechanistically possible, requires surgical excision of the aneurysm sac, with several potential drawbacks. First, the majority of cerebral aneurysms are preferentially treated using minimally invasive endovascular devices rather than surgery, reducing the number of potential aneurysms that are surgically harvested and studied ex vivo.28 More importantly, because the aneurysm sac must be removed from the parent vessel in an ex vivo study, its precise geometry is difficult to re-create in a laboratory setting, and direct correlation of aneurysm tissue with areas of hemodynamic stress defined by CFD simulations is difficult, impractical, and carries significant uncertainty. Existing ex vivo studies of human aneurysm tissue have thus primarily focused on structural phenomena, such as inflammatory cell infiltrate and differential wall thicknesses.29 Only a few studies30,31 have directly related CFD-identified areas of hemodynamic stresses to structural changes in human cerebral aneurysms harvested ex vivo. They found that wall stiffness, inflammation, and abnormal collagen organization were generally associated with regions of elevated WSS, while mural thrombus and atherosclerosis were associated with regions of low WSS. These observations are consistent with the downstream effects of key aneurysm-related vascular responses, though specific quantification of the endothelial transcriptional profile was not performed.

Thus far, only a few studies have attempted to relate endothelial response to hemodynamic stress in a rigorous manner. One study used a simple, idealized, and non-patient aneurysm geometry of a sidewall aneurysm to demonstrate upregulation of VCAM-1 in areas of low WSS in the aneurysm dome.15 This study is an important proof of concept and demonstrates the important link between low WSS and VCAM-1 expression, though the geometric scale (0.5 mm parent vessel and 0.5 mm aneurysm dome) was much smaller than that of actual arteries of the circle of Willis. Another study directly observed the endothelial morphological organization, but not the gene expression response, to hemodynamic stresses by populating a 3D-printed model of a human cerebral aneurysm with endothelial cells.13 Using a bovine endothelial cell line, their work showed alignment of endothelial cells with the direction of flow within the parent vessel, but did not have a well-defined elongation shape or direction in areas of complex flow within the aneurysm sac. While demonstrating that 3D-printed models can be populated with endothelial cells, this work only considered a single aneurysm under steady (non-physiologic and non-pulsatile) flow, which reduces its clinical relevance.2 In addition, their study had no molecular insights for the endothelial cell response in a human cerebral aneurysm. An abstract by the same group describes such analysis taking place, but the results of this analysis have not been peer-reviewed or published.32

Our study takes additional steps towards a more realistic simulation of the effect of aneurysm hemodynamics on endothelial cells. Our method builds anatomically accurate, 3D-printed, patient-specific intracranial aneurysm flow phantoms, cultures human endothelial cells in them under pulsatile flow, and characterizes the mRNA expression of these endothelial cells in different regions of the aneurysm, in addition to cell morphology changes demonstrated in previous studies. More importantly, we are able to make direct correlations with CFD simulations of hemodynamics of the same aneurysm anatomy. The endothelial cell morphology in our model had similar trends to those previously reported, including alignment in areas of organized flow, and lack of defined shape or direction in areas of complex, non-unidirectional flow.11,13 Importantly, we found that ADAMTS-1 and NOS3 levels were reduced (figure 4) in the endothelial cells harvested from the aneurysmal dome, compared with the proximal parent vessel, while GJA4 was reduced in the distal parent vessel. The CFD simulations of the same aneurysm found predominantly very low WSS and WSSG throughout the aneurysm dome (figure 1C). Thus, our work suggests a relationship between ADAMTS-1, NOS3, and aneurysmal endothelial mechanotransduction (including downregulation in regions of low WSS),8,9,22 while reduced GJA4 is a marker for oscillatory flow in the distal parent vessel.33

This exploratory, pilot study has several limitations. First, flow through the 3D-printed model, while pulsatile, did not use a patient-specific physiological waveform. Future studies will incorporate patient-specific pulsatile flow into such experiments by introducing a programmable pump in the incubator.

Second, our study used human umbilical vein endothelial cells and human carotid endothelial cells and did not include smooth muscle cells. Future studies will incorporate vascular smooth muscle cells, which play an important role in cerebrovascular biology.34

Third, we performed genetic analysis on endothelial cells after only 24 hours under flow. This cut-off point was used because vascular biology factors related to hemodynamic stress have demonstrated maximal change in response to such flow before 24 hours,35 and cell detachment can become common at longer flow exposures (data not shown).

Fourth, the aneurysm’s anatomy, while a precise replica of an actual patient’s aneurysm, does not contain the degree of complex geometry typically seen in many aneurysms of the anterior circulation. In addition, the photoreactive resin used to create this model is difficult to dissolve, precluding its use in more complex geometries, and these models require manual smoothing to ensure that no microscopic ridges have developed. We have recently developed a more robust technique using wax,36 which will permit more geometrically varied patient-specific 3D models, permitting smoothing of the wax cast and resulting in dimensional accuracy of ~100 μm. More complex aneurysm geometries may also require additional plasma treatment optimization to ensure complete hydrophilization of the luminal surface. The diffusion constant and very low etch rate of oxygen plasma ions on PDMS suggest that this strategy can be applied to complex aneurysms without compromising model integrity.37,38

Finally, we found no consistently significant change in gene expression in this particular patient-specific aneurysm for most of the genes studied, and no quantitative morphological changes. A larger cohort of 3D-printed, patient-specific aneurysm models using this method will enable experiments over a wide variety of cerebral aneurysms with differential areas of high and low WSS and WSSG, necessary to relate specific areas of hemodynamic stress with endothelial expression across a wide range of patterns and maximum/minimum values in a statistically significant manner.

CONCLUSION

Creating 3D-printed cerebral aneurysms populated with living human endothelial cells is feasible, and morphologic and genetic analysis of the cells harvested after 24 hours of exposure to physiological pulsatile flow can quantify the endothelial response to hemodynamic stress.

Acknowledgments

Funding Thiswork was supported by the National institutes of Health/National institute of Neurological Disorders and Stroke grants R01NS088072 and R01NS105692.

Footnotes

Correction notice Since this article was first published online, figure 4 has been replaced.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No unpublished data available.

REFERENCES

  • 1.Jou LD, Quick CM, Young WL, et al. Computational approach to quantifying hemodynamic forces in giant cerebral aneurysms. AJNR Am J Neuroradiol 2003;24:1804–10. [PMC free article] [PubMed] [Google Scholar]
  • 2.Xiang J, Natarajan SK, Tremmel M, et al. Hemodynamic-morphologic discriminants for intracranial aneurysm rupture. Stroke 2011;42:144–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Meng H, Tutino VM, Xiang J, et al. High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis. AJNR Am J Neuroradiol 2014;35:1254–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Tzima E, Irani-Tehrani M, Kiosses WB, et al. A mechanosensoiy complex that mediates the endothelial cell response to fluid shear stress. Nature 2005;437:426–31. [DOI] [PubMed] [Google Scholar]
  • 5.Hashimoto T, Meng H, Young WL. Intracranial aneurysms: links among inflammation, hemodynamics and vascular remodeling. Neurol Res 2006;28:372–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Malek AM, Alper SL, Izumo S. Hemodynamic shear stress and its role in atherosclerosis. JAMA 1999;282:2035–42. [DOI] [PubMed] [Google Scholar]
  • 7.Walpola PL, Gotlieb AI, Cybulsky MI, et al. Expression of ICAM-1 and VCAM-1 and monocyte adherence in arteries exposed to altered shear stress. Arterioscler Thromb Vasc Biol 1995;15:2–10. [DOI] [PubMed] [Google Scholar]
  • 8.Dolan JM, Meng H, Sim FJ, et al. Differential gene expression by endothelial cells under positive and negative streamwise gradients of high wall shear stress. Am J Physiol Cell Physiol 2013;305:C854–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Dolan JM, Sim FJ, Meng H, et al. Endothelial cells express a unique transcriptional profile under very high wall shear stress known to induce expansive arterial remodeling. Am J Physiol Cell Physiol 2012;302:C1109–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Aoki T, Yamamoto K, Fukuda M, et al. Sustained expression of MCP-1 by low wall shear stress loading concomitant with turbulent flow on endothelial cells of intracranial aneurysm. Acta Neuropathol Commun 2016;4:48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Chiu JJ, Wang DL, Chien S, et al. Effects of disturbed flow on endothelial cells. J Biomech Eng 1998;120:2–8. [DOI] [PubMed] [Google Scholar]
  • 12.Yoshino D, Sakamoto N, Sato M. Fluid shear stress combined with shear stress spatial gradients regulates vascular endothelial morphology. Integr Biol 2017;9:584–94. [DOI] [PubMed] [Google Scholar]
  • 13.Kaneko N, Mashiko T, Namba K, et al. A patient-specific intracranial aneurysm model with endothelial lining: a novel in vitro approach to bridge the gap between biology and flow dynamics. J Neurointerv Surg 2018;10:306–9. [DOI] [PubMed] [Google Scholar]
  • 14.Kolega J, Gao L, Mandelbaum M, et al. Cellular and molecular responses of the basilar terminus to hemodynamics during intracranial aneurysm initiation in a rabbit model. J Vasc Res 2011;48:429–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Mannino RG, Myers DR, Ahn B, et al. “Do-it-yourself in vitro vasculature that recapitulates in vivo geometries for investigating endothelial-blood cell interactions”. Sci Rep 2015;5:12401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ghriallais RN, McNamara L, Bruzzi M. Comparison of in vitro human endothelial cell response to self-expanding stent deployment in a straight and curved peripheral artery simulator. J R Soc Interface 2013;10:20120965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Carrasco F, Chornet E, Overend RP, et al. A generalized correlation for the viscosity of dextrans in aqueous solutions as a function of temperature, concentration, and molecular weight at low shear rates. J Appl Polym Sci 1989;37:2087–98. [Google Scholar]
  • 18.Levitt MR, Barbour MC, Rolland du Roscoat S, et al. Computational fluid dynamics of cerebral aneurysm coiling using high-resolution and high-energy synchrotron X-ray microtomography: comparison with the homogeneous porous medium approach. J Neurointerv Surg 2017;9:00.1–00. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Cunningham KS, Gotlieb AI. The role of shear stress in the pathogenesis of atherosclerosis. Lab Invest 2005;85:9–23. [DOI] [PubMed] [Google Scholar]
  • 20.O’Keeffe LM, Muir G, Piterina AV, et al. Vascular cell adhesion molecule-1 expression in endothelial cells exposed to physiological coronary wall shear stresses. J Biomech Eng 2009;131:081003. [DOI] [PubMed] [Google Scholar]
  • 21.Rojas HA, Fernandes K, Ottone MR, et al. Levels of MMP-9 in patients with intracranial aneurysm: relation with risk factors, size and clinical presentation. Clin Biochem 2018;55:63–8. [DOI] [PubMed] [Google Scholar]
  • 22.Liaw N, Fox JM, Siddiqui AH, et al. Endothelial nitric oxide synthase and superoxide mediate hemodynamic initiation of intracranial aneurysms. PLoS One 2014;9:e101721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Sho E, Sho M, Nanjo H, et al. Hemodynamic regulation of CD34+ cell localization and differentiation in experimental aneurysms. Arterioscler Thromb Vasc Biol 2004;24:1916–21. [DOI] [PubMed] [Google Scholar]
  • 24.Zeng Z, Kallmes DF, Durka MJ, et al. Sensitivity of CFD based hemodynamic results in rabbit aneurysm models to idealizations in surrounding vasculature. J Biomech Eng 2010;132:091009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Suzuki T, Takao H, Suzuki T, et al. Determining the oresence of thin-walled regions at high-pressure areas in unruptured cerebral aneurysms by using computational fluid dynamics. Neurosurgery 2016;79:589–95. [DOI] [PubMed] [Google Scholar]
  • 26.Sugiyama S, Niizuma K, Nakayama T, et al. Relative residence time prolongation in intracranial aneurysms: a possible association with atherosclerosis. Neurosurgery 2013;73:767–76. [DOI] [PubMed] [Google Scholar]
  • 27.Furukawa K, ishida F, Tsuji M, et al. Hemodynamic characteristics of hyperplastic remodeling lesions in cerebral aneurysms. PLoS One 2018;13:e0191287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Lin N, Cahill KS, Frerichs KU, et al. Treatment of ruptured and unruptured cerebral aneuiysms in the USA: a paradigm shift. J Neurointerv Surg 2018;10:i69–i76. [DOI] [PubMed] [Google Scholar]
  • 29.Frösen J, Piippo A, Paetau A, et al. Remodeling of saccular cerebral artery aneurysm wall is associated with rupture: histological analysis of 24 unruptured and 42 ruptured cases. Stroke 2004;35:2287–93. [DOI] [PubMed] [Google Scholar]
  • 30.Cebral J, Ollikainen E, Chung BJ, et al. Flow conditions in the intracranial aneurysm lumen are associated with inflammation and degenerative changes of the aneurysm wall. AJNR Am J Neuroradiol 2017;38:119–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Cebral JR, Duan X, Gade PS, et al. Regional mapping of flow and wall characteristics of intracranial aneurysms. Ann Biomed Eng 2016;44:3553–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Kaneko N, Tateshima S, Chen D, et al. Abstract WMP35: Endothelialized 3D cerebrovascular modeling: a novel in vitro approach to study gene expression in realistic vascular geometry. Stroke 2018;49. [Google Scholar]
  • 33.Pfenniger A, Wong C, Sutter E, et al. Shear stress modulates the expression of the atheroprotective protein Cx37 in endothelial cells. J Mol Cell Cardiol 2012;53:299–309. [DOI] [PubMed] [Google Scholar]
  • 34.Tanweer O, Wilson TA, Metaxa E, et al. A comparative review of the hemodynamics and pathogenesis of cerebral and abdominal aortic aneurysms: lessons to learn from each other. J Cerebrovasc Endovasc Neurosurg 2014;16:335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Brooks AR, Lelkes PI, Rubanyi GM. Gene expression profiling of human aortic endothelial cells exposed to disturbed flow and steady laminar flow. Physiol Genomics 2002;9:27–41. [DOI] [PubMed] [Google Scholar]
  • 36.Chivukula VK, Levitt MR, Clark A, et al. Reconstructing patient-specific cerebral aneurysm vasculature for in vitro investigations and treatment efficacy assessments. J Clin Neurosci 2019;61:153–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Tan SH, Nguyen NT, Chua YC, et al. Oxygen plasma treatment for reducing hydrophobicity of a sealed polydimethylsiloxane microchannel. Biomicrofluidics 2010:4:032204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Garra J, Long T, Currie J, et al. Dry etching of polydimethylsiloxane for microfluidic systems. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 2002;20:975–82. [Google Scholar]

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