Gait analysis of a post induced traumatic spinal cord injury porcine model

Contribution of impactor misalignment to the neurofunctional variability in porcine spinal cord contusion models

Traumatic spinal cord lesions studies are often carried out with animal models or numerical simulations. Unfortunately, animal models usually present a high variability in severity and type of neurofunctional impairments following impact surgery. We postulate that the variability of outcomes is strongly dependent on the positioning and alignment of the impactor during the contusion. A finite elements model of the spinal cord, predicting the action potential (AP) conduction alteration, was proposed and used to perform nine numerical simulations of a 50 g weight dropped from 200 mm on the exposed spinal cord in its spinal canal. Simulations followed a 32 factorial design with impactor eccentricity and spinal cord tilt angle as factors on two outcomes: injured spinal cord area (AP < 10 % of its baseline, 1h post-injury), and asymmetry of injury (ratio of right/left injured area of both half spinal cord). Eccentricity contributed highly and significantly on both outcomes, but not tilt angle. Damaged axons were found in conscious motor, sensory, and unconscious proprioception tracts. Variability in impactor alignment beyond ±6.2 % of the spinal canal width affects neurofunctional outcomes, and careful assessment of the impactor course is therefore key when producing spinal cord injury by contusion.Clinical Relevance- A precision value is proposed to mitigate the contribution of impactor misalignment to neurofunctional variability in animal models, allowing the reduction of animal used in research. The proposed method of action potential conduction assessment could easily be implanted in human numerical models for the cross-study of patient's cases.

A workflow for automatic, high precision livestock diagnostic screening of locomotor kinematics

Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g., body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets (Sus domesticus) were filmed from lateral perspective during their first 10 h of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy was validated for strides from piglets of normal birth weight (i.e., the category it was trained on), but the models infer the body mass and size of low birth weight (LBW) piglets (which were left out of training, out-of-sample inference) to be "normal." The age of some (but not all) low birth weight individuals was underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.