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Mechanical, biochemical, and morphological topography of ovine knee cartilage.

Makayla RischJeremiah T EasleyErin G McCreadyKevin L TroyerJames W JohnsonBenjamin C GadomskiKirk C McGilvraySuwimol TangtrongsupBrad B Nelson
Published in: Journal of orthopaedic research : official publication of the Orthopaedic Research Society (2020)
The knee is the most common site for translational cartilage research in sheep, though topographic features of articular cartilage across surfaces are unspecified. We aimed to characterize the mechanical, morphological, and biochemical properties of articular cartilage across ovine knee surfaces and document variations between and within surface locations. Regions of interest (ROIs) were delineated across surfaces of 10 healthy ovine knees. Articular cartilage at each ROI was measured for creep indentation, thickness, and glycosaminoglycan (GAG) and collagen content. Variables were compared between surface locations (trochlea, and lateral [LFC] and medial [MFC] femoral condyles) and between ROIs within each surface location. Correlations between variables were also assessed. Articular surface location had a significant effect on creep (P < .0001), thickness (P < .0001), and collagen (P = .0007), but not GAG (P = .28). Significant differences in percent creep between ROIs were found within the LFC (P < .0001), MFC (P < .0001), and trochlea (P = .0002). Cartilage thickness was different between ROIs within the LFC, MFC, and trochlea (all P < .0001). The LFC (P = .002) and trochlea (P = .01) each had significant differences in GAG between ROIs. Collagen content between ROIs was different within the LFC (P = .0003), MFC (P = .0005), and trochlea (P < .0001). Collagen content was correlated with thickness (r = -.55), percent creep (r = .47), and GAG (r = -.21). Percent creep was correlated with thickness (r = -.64) and GAG (r = -.19). Topographic variations in mechanical, morphological, and biochemical properties exist across knee cartilage surfaces in sheep. Recognition of this variability is important to optimize study protocols and improve accuracy of results.
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