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Research Papers

Transport of Neutral Solute Across Articular Cartilage: The Role of Zonal Diffusivities

[+] Author and Article Information
V. Arbabi

Department of Biomechanical Engineering,
Faculty of Mechanical, Maritime,
and Materials Engineering,
Delft University of Technology (TU Delft),
Mekelweg 2,
Delft 2628CD, The Netherlands
e-mail: v.arbabi@tudelft.nl; v.arbabi@gmail.com

B. Pouran

Department of Orthopedics, UMC Utrecht,
Heidelberglaan 100,
Utrecht 3584CX, The Netherlands
Department of Biomechanical Engineering,
Faculty of Mechanical, Maritime,
and Materials Engineering,
Delft University of Technology (TU Delft),
Mekelweg 2,
Delft 2628CD, The Netherlands

H. Weinans

Department of Orthopedics and
Department of Rheumatology,
UMC
Utrecht, Heidelberglaan 100,
Utrecht 3584CX, The Netherlands
Department of Biomechanical Engineering,
Faculty of Mechanical, Maritime, and Materials Engineering,
Delft University of Technology (TU Delft),
Mekelweg 2, 2628CD,
Delft, The Netherlands

A. A. Zadpoor

Department of Biomechanical Engineering,
Faculty of Mechanical, Maritime,
and Materials Engineering,
Delft University of Technology (TU Delft),
Mekelweg 2,
Delft 2628CD, The Netherlands

1Corresponding author.

Manuscript received December 15, 2014; final manuscript received March 10, 2015; published online June 2, 2015. Assoc. Editor: Pasquale Vena.

J Biomech Eng 137(7), 071001 (Jul 01, 2015) (9 pages) Paper No: BIO-14-1629; doi: 10.1115/1.4030070 History: Received December 15, 2014; Revised March 10, 2015; Online June 02, 2015

Transport of solutes through diffusion is an important metabolic mechanism for the avascular cartilage tissue. Three types of interconnected physical phenomena, namely mechanical, electrical, and chemical, are all involved in the physics of transport in cartilage. In this study, we use a carefully designed experimental-computational setup to separate the effects of mechanical and chemical factors from those of electrical charges. Axial diffusion of a neutral solute (Iodixanol) into cartilage was monitored using calibrated microcomputed tomography (micro-CT) images for up to 48 hr. A biphasic-solute computational model was fitted to the experimental data to determine the diffusion coefficients of cartilage. Cartilage was modeled either using one single diffusion coefficient (single-zone model) or using three diffusion coefficients corresponding to superficial, middle, and deep cartilage zones (multizone model). It was observed that the single-zone model cannot capture the entire concentration-time curve and under-predicts the near-equilibrium concentration values, whereas the multizone model could very well match the experimental data. The diffusion coefficient of the superficial zone was found to be at least one order of magnitude larger than that of the middle zone. Since neutral solutes were used, glycosaminoglycan (GAG) content cannot be the primary reason behind such large differences between the diffusion coefficients of the different cartilage zones. It is therefore concluded that other features of the different cartilage zones such as water content and the organization (orientation) of collagen fibers may be enough to cause large differences in diffusion coefficients through the cartilage thickness.

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Figures

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Fig. 1

The specimen used in the diffusion experiments

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Fig. 2

The sequence of images for one sample specimen and the different image processing steps used for calculating the average gray values

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Fig. 3

Schematic drawings of the single-zone (a) and multizone (b) for computational models

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Fig. 4

Analytical (symbols) and finite element model (solid lines) solutions for finite well-stirred bath: normalized concentration (C/C∞) is plotted versus η = Dt/l for different locations within cartilage

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Fig. 5

Experimental (symbols) and simulated (dashed-lines) concentration versus time for conditions A–C and samples 1(a)–3(c). Computational models (single-zone) were fitted to all experimental data points.

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Fig. 6

Experimental (symbols) and simulated (dashed-lines) concentration versus time for conditions A–C and samples 1(a)–3(c). Computational models (singlezone) were fitted only to early time experimental data points.

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Fig. 7

Experimental (symbols) and simulated (dashed-lines) concentration versus time for conditions A–C and samples 1(a)–3(c). Computational models (multizone) were fitted to all experimental data points.

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