Contribution of collagen degradation and proteoglycan depletion to cartilage degeneration in primary and secondary osteoarthritis: an in silico study.

OBJECTIVES: Current experimental approaches cannot elucidate the effect of maladaptive changes on the main cartilage constituents during the degeneration process in osteoarthritis (OA). In silico approaches, however, allow creating 'virtual knock-out' cases to elucidate these effects in a constituent-specific manner. We used such an approach to study the main mechanisms of cartilage degeneration in different mechanical loadings associated with the following OA etiologies: (1) physiological loading of degenerated cartilage, (2) injurious loading of healthy intact cartilage and (3) physiological loading of cartilage with a focal defect. METHODS: We used the recently developed Cartilage Adaptive REorientation Degeneration (CARED) framework to simulate cartilage degeneration associated with primary and secondary OA (OA cases (1)-(3)). CARED incorporates numerical description of tissue-level cartilage degeneration mechanisms in OA, namely, collagen degradation, collagen reorientation, fixed charged density loss and tissue hydration increase following mechanical loading. We created 'virtual knock-out' scenarios by deactivating these degenerative processes one at a time in each of the three OA cases. RESULTS: In the injurious loading of intact and physiological loading of degenerated cartilage, collagen degradation drives degenerative changes through fixed charge density loss and tissue hydration rise. In contrast, the two later mechanisms were more prominent in the focal defect cartilage model. CONCLUSION: The virtual knock-out models reveal that injurious loading to intact cartilage and physiological loading to degenerated cartilage induce initial degenerative changes in the collagen network, whereas, in the presence of a focal cartilage defect, mechanical loading initially causes proteoglycans (PG) depletion, before changes in the collagen fibril network occur.

Cartilage-on-cartilage contact: effect of compressive loading on tissue deformations and structural integrity of bovine articular cartilage.

OBJECTIVE: This study aims to characterize the deformations in articular cartilage under compressive loading and link these to changes in the extracellular matrix constituents described by magnetic resonance imaging (MRI) relaxation times in an experimental model mimicking in vivo cartilage-on-cartilage contact. DESIGN: Quantitative MRI images, T1, T2 and T1ρ relaxation times, were acquired at 9.4T from bovine femoral osteochondral explants before and immediately after loading. Two-dimensional intra-tissue displacement and strain fields under cyclic compressive loading (350N) were measured using the displacement encoding with stimulated echoes (DENSE) method. Changes in relaxation times in response to loading were evaluated against the deformation fields. RESULTS: Deformation fields showed consistent patterns among all specimens, with maximal strains at the articular surface that decrease with tissue depth. Axial and transverse strains were maximal around the center of the contact region, whereas shear strains were minimal around the contact center but increased towards contact edges. A decrease in T2 and T1ρ was observed immediately after loading whereas the opposite was observed for T1. No correlations between cartilage deformation patterns and changes in relaxation times were observed. CONCLUSIONS: Displacement encoding combined with relaxometry by MRI can noninvasively monitor the cartilage biomechanical and biochemical properties associated with loading. The deformation fields reveal complex patterns reflecting the depth-dependent mechanical properties, but intra-tissue deformation under compressive loading does not correlate with structural and compositional changes. The compacting effect of cyclic compression on the cartilage tissue was revealed by the change in relaxation time immediately after loading.

GPGPU-based explicit finite element computations for applications in biomechanics: the performance of material models, element technologies, and hardware generations.

Finite element (FE) simulations are increasingly valuable in assessing and improving the performance of biomedical devices and procedures. Due to high computational demands such simulations may become difficult or even infeasible, especially when considering nearly incompressible and anisotropic material models prevalent in analyses of soft tissues. Implementations of GPGPU-based explicit FEs predominantly cover isotropic materials, e.g. the neo-Hookean model. To elucidate the computational expense of anisotropic materials, we implement the Gasser-Ogden-Holzapfel dispersed, fiber-reinforced model and compare solution times against the neo-Hookean model. Implementations of GPGPU-based explicit FEs conventionally rely on single-point (under) integration. To elucidate the expense of full and selective-reduced integration (more reliable) we implement both and compare corresponding solution times against those generated using underintegration. To better understand the advancement of hardware, we compare results generated using representative Nvidia GPGPUs from three recent generations: Fermi (C2075), Kepler (K20c), and Maxwell (GTX980). We explore scaling by solving the same boundary value problem (an extension-inflation test on a segment of human aorta) with progressively larger FE meshes. Our results demonstrate substantial improvements in simulation speeds relative to two benchmark FE codes (up to 300[Formula: see text] while maintaining accuracy), and thus open many avenues to novel applications in biomechanics and medicine.

Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach.

Accurate estimation of peak wall stress (PWS) is the crux of biomechanically motivated rupture risk assessment for abdominal aortic aneurysms aimed to improve clinical outcomes. Such assessments often use the finite element (FE) method to obtain PWS, albeit at a high computational cost, motivating simplifications in material or element formulations. These simplifications, while useful, come at a cost of reliability and accuracy. We achieve research-standard accuracy and maintain clinically applicable speeds by using novel computational technologies. We present a solution using our custom finite element code based on graphics processing unit (GPU) technology that is able to account for added complexities involved with more physiologically relevant solutions, e.g. strong anisotropy and heterogeneity. We present solutions up to 17× faster relative to an established finite element code using state-of-the-art nonlinear, anisotropic and nearly-incompressible material descriptions. We show a realistic assessment of the explicit GPU FE approach by using complex problem geometry, biofidelic material law, double-precision floating point computation and full element integration. Due to the increased solution speed without loss of accuracy, shown on five clinical cases of abdominal aortic aneurysms, the method shows promise for clinical use in determining rupture risk of abdominal aortic aneurysms.

Non-invasive, energy-based assessment of patient-specific material properties of arterial tissue.

The mechanical properties of human biological tissue vary greatly. The determination of arterial material properties should be based on experimental data, i.e. diameter, length, intramural pressure, axial force and stress-free geometry. Currently, clinical data provide only non-invasively measured pressure-diameter data for superficial arteries (e.g. common carotid and femoral artery). The lack of information forces us to take into account certain assumptions regarding the in situ configuration to estimate material properties in vivo. This paper proposes a new, non-invasive, energy-based approach for arterial material property estimation. This approach is compared with an approach proposed in the literature. For this purpose, a simplified finite element model of an artery was used as a mock experimental situation. This method enables exact knowledge of the actual material properties, thereby allowing a quantitative evaluation of material property estimation approaches. The results show that imposing conditions on strain energy can provide a good estimation of the material properties from the non-invasively measured pressure and diameter data.

Cyclically stretching developing tissue in vivo enhances mechanical strength and organization of vascular grafts.

Tissue-engineered vascular grafts must have qualities that rival native vasculature, specifically the ability to remodel, the expression of functional endothelial components and a dynamic and functional extracellular matrix (ECM) that resists the forces of the arterial circulation. We have developed a device that when inserted into the peritoneal cavity, attracts cells around a tubular scaffold to generate autologous arterial grafts. The device is capable of cyclically stretching (by means of a pulsatile pump) developing tissue to increase the mechanical strength of the graft. Pulsed (n=8) and unpulsed (n=8) devices were implanted for 10 days in Lovenaar sheep (n=8). Pulsation occurred for a period of 5-8 days before harvest. Thick unadhered autologous tissue with cells residing in a collagen ECM was produced in all devices. Collagen organization was greater in the circumferential direction of pulsed tissue. Immunohistochemical labelling revealed the hematopoietic origin of >90% cells and a significantly higher coexpression with vimentin in pulsed tissue. F-actin expression, mechanical failure strength and strain were also significantly increased by pulsation. Moreover, tissue could be grafted as carotid artery patches. This paper shows that unadhered tissue tubes with increased mechanical strength and differentiation in response to pulsation can be produced with every implant after a period of 10 days. However, these tissue tubes require a more fine-tuned exposure to pulsation to be suitable for use as vascular grafts.