Étude des biomarqueurs lésionnels de la moelle épinière porcine

Abstract

The recent and growing use in numerous studies of the porcine model as a translational model for the study of Traumatic Spinal Cord Injury (TSCI) shows the potential of this animal for preclinical models. The monitoring of remission of TSCI, induced by using different methods, is done through the study and analysis of biomarkers. These biomarkers are numerous : gait analysis, Magnetic Resonance Imaging (MRI), histology, neurological and physiological variables, evoked somatosensory potential. To our knowledge, the analysis of the gait resulting from TSCI is carried out in an exclusively qualitative way, and quantitative MRI is only used for humans or rats. This master’s project presents two quantifiable approaches for the evaluation of the evolution of a pig recovery following a controlled TSCI. A first approach based on a quantitative gait analysis of a pig, which will help to assess the evolution of the functional recovery of the animal in a more precise and quantifiable way than the qualitative approach alone. A second approach based on the analysis of quantifiable parameters that MRI can offer. This thesis is the continuation of previous work, especially those from Francis Cliche and Daniel Moore who developed, designed, manufactured and validated the motorized experimental test bench for reproducing controlled contusion on the pig. It is also based on kinematics acquisitions made by Anthony Léveillée and the MRI acquisitions made by Lucien Diotalevi. The first step was to establish a plan for exploiting these data, from the extraction to their interpretation. The raw data were processed to correct camera tracking errors and make them interpretable in order to eventually extract the key parameters of the pig’s gait. Secondly, quantifiable parameters were extracted from the MRI images previously collected according to the acquisitions made. They have been interpreted. Finally, from this work emerges future recommendations to correct errors and improve the evaluation of MRI and kinematic monitoring.

Date	9 Sept 2020
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Yvan Petit (Supervisor) & Éric Wagnac (Co-supervisor)