MOVEMENT DATA ANALYSIS AS A NOVEL PREDICTIVE TECHNOLOGY FOR TENDON REPAIR

Abstract

Digital Ventilated Cages (DVC) offer an innovative technology to obtain accurate movement data from a single mouse over time [1]. Thus, they could be used to determine the occurrence of a tendon damage event as well as inform on tissue regeneration [2,3]. Therefore, using the mouse model of tendon experimental damage, in this study it has been tested whether the recovery of tissue microarchitecture and of extracellular matrix (ECM) correlates with the motion data collected through this technology.Mice models were used to induce acute injury in Achilles tendons (ATs), while healthy ones were used as control. During the healing process, the mice were housed in DVC cages (Tecniplast) to monitor animal welfare and to study biomechanics assessing movement activity, an indicator of the recovery of tendon tissue functionality. After 28 days, the AT were harvested and assessed for their histological and immunohistochemical properties to obtain a total histological score (TSH) that was then correlated to the movement data.DVC cages showed the capacity to distinguish activity patterns in groups from the two different conditions. The data collected showed that the mice with access to the mouse wheel had a higher activity as compared to the blocked wheel group, which suggests that the extra movement during tendon healing improved motion ability. The histological results showed a clear difference between different analyzed groups. The bilateral free wheel group showed the best histological recovery, offering the highest TSH score, thus confirming the results of the DVC cages and the correlation between movement activity and structural recovery.Data obtained showed a correlation between TSH and the DVC cages, displaying structural and movement differences between the tested groups. This successful correlation allows the usage of DVC type cages as a non-invasive method to predict tissue regeneration and recovery.Acknowledgements: This research is part of the P4FIT project ESR13, funded by the H2020-ITN-EJD MSCA grant agreement No.955685.