The spatio-temporal mechanical environment of healthy and injured human cartilage during sustained activity and its role in cartilage damage

Miramini, Saeed; Smith, David W.; Zhang, Lihai; Gardiner, Bruce S.

doi:10.1016/j.jmbbm.2017.05.018

Cited by 37 publications

(23 citation statements)

References 47 publications

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“…due to partial or total meniscectomy (Bedi et al 2010(Bedi et al , 2012), it may first cause increased contact and fluid pressure and then a relatively rapid consolidation. This can also be seen as a decrease of fluid pressure slightly after the application of the load (Kazemi et al 2013;Miramini et al 2017). If cartilage is additionally damaged, its ability to sustain fluid pressurization for a long periods of time may be compromised (Dabiri and Li 2015;Miramini et al 2017), which may subject the tissue to excessive strains leading to matrix damage and chondrocyte death.…”

Section: Future Developmentsmentioning

confidence: 99%

“…This can also be seen as a decrease of fluid pressure slightly after the application of the load (Kazemi et al 2013;Miramini et al 2017). If cartilage is additionally damaged, its ability to sustain fluid pressurization for a long periods of time may be compromised (Dabiri and Li 2015;Miramini et al 2017), which may subject the tissue to excessive strains leading to matrix damage and chondrocyte death. These phenomena may further contribute to the collagen remodeling and tissue failure, and should be investigated in more detail.…”

Section: Future Developmentsmentioning

confidence: 99%

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A computational algorithm to simulate disorganization of collagen network in injured articular cartilage

Tanska

Julkunen

Korhonen

2017

Biomech Model Mechanobiol

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Cartilage defects are a known risk factor for osteoarthritis. Estimation of structural changes in these defects could help us to identify high risk defects and thus to identify patients that are susceptible for the onset and progression of osteoarthritis. Here, we present an algorithm combined with computational modeling to simulate the disorganization of collagen fibril network in injured cartilage. Several potential triggers for collagen disorganization were tested in the algorithm following the assumption that disorganization is dependent on the mechanical stimulus of the tissue. We found that tensile tissue stimulus alone was unable to preserve collagen architecture in intact cartilage as collagen network reoriented throughout the cartilage thickness. However, when collagen reorientation was based on both tensile tissue stimulus and tensile collagen fibril strains or stresses, the collagen network architecture was preserved in intact cartilage. Using the same approach, substantial collagen reorientation was predicted locally near the cartilage defect and particularly at the cartilage-bone interface. The developed algorithm was able to predict similar structural findings reported in the literature that are associated with experimentally observed remodeling in articular cartilage. The proposed algorithm, if further validated, could help to predict structural changes in articular cartilage following post-traumatic injury potentially advancing to impaired cartilage function.

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Section: Future Developmentsmentioning

confidence: 99%

Section: Future Developmentsmentioning

confidence: 99%

A computational algorithm to simulate disorganization of collagen network in injured articular cartilage

Tanska

Julkunen

Korhonen

2017

Biomech Model Mechanobiol

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“…Another parameter, maximum principal stress, is often analyzed from FE models of the knee joint to reflect the point of cartilage failure [6,45,46] and the initiation and progression of OA [7,8]. In this study, even though maximum principal stresses showed more variation between the models when compared to strains, the mean difference in the absolute values of this parameter was less than 1 MPa.…”

Section: Discussionmentioning

confidence: 76%

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint

Myller

Korhonen

Töyräs

et al. 2020

Journal of Biomechanical Engineering

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Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

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“…In addition, the structural properties of the scaffold, such as porosity and permeability, are critically important as they affect the physiological functions of the implant (e.g. cell migration, nutrient transport) as well as its biointegration (Haghpanahi and Miramini, 2008;Miramini et al, 2017;Pilliar et al, 1975).…”

Section: ) Structural and Mechanical Characterisation Of Am Materialmentioning

confidence: 99%

The status and challenges of replicating the mechanical properties of connective tissues using additive manufacturing

Miramini

Fegan

Green

et al. 2020

Journal of the Mechanical Behavior of Biomedical Materials

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show abstract

The spatio-temporal mechanical environment of healthy and injured human cartilage during sustained activity and its role in cartilage damage

Cited by 37 publications

References 47 publications

A computational algorithm to simulate disorganization of collagen network in injured articular cartilage

A computational algorithm to simulate disorganization of collagen network in injured articular cartilage

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint

The status and challenges of replicating the mechanical properties of connective tissues using additive manufacturing

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