Use of a novel magnetically actuated compression system to study the temporal dynamics of axial and lateral strain in human osteochondral plugs.

The high water content of articular cartilage allows this biphasic tissue to withstand large compressive loads through fluid pressurization. The system presented here, termed the "MagnaSquish", provides new capabilities for quantifying the effect of rehydration on cartilage behavior during cyclic loading. An imbalanced rate of fluid exudation during load and fluid re-entry during recovery can lead to the accumulation of strain during successive loading cycles - a phenomenon known as ratcheting. Typical experimental systems for cartilage biomechanics use continuous contact between the platen and sample, which may affect tissue rehydration by compressing the top layer of cartilage and slowing fluid re-entry. To address this limitation, we developed a magnetically actuated device that provides full lift-off of the platen in between loading cycles. We investigated strain accumulation in cadaveric human osteochondral plugs during 750 loading cycles, with two dimensional profiles of the cartilage captured at 30 frames per second throughout loading and 10 min of additional free swelling recovery. Axial and lateral strain measurements were extracted from the tissue profiles using a UNet-based deep learning algorithm to circumvent manual tracing. We observed increased axial strain accumulation with shorter inter-cycle recovery, with static loading serving as the extreme case of zero recovery. The loading waveform during the 750 cycles dictated the pace of the recovery during the extended free swelling period, as shorter inter-cycle recovery led to more persistent axial strain accumulation for up to five minutes. This work showcases the importance of fluid re-entry in resisting strain accumulation during cyclical compression.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)