Quantitation of Gait and Stance Alterations Due to Monosodium Iodoacetate-induced Knee Osteoarthritis in Yucatan Swine

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.

A systematic review of porcine models in translational pain research

Translating basic pain research from rodents to humans has proven to be a challenging task. Efforts have been made to develop preclinical large animal models of pain, such as the pig. However, no consistent overview and comparison of pig models of pain are currently available. Therefore, in this review, our primary aim was to identify the available pig models in pain research and compare these models in terms of intensity and duration. First, we systematically searched Proquest, Scopus and Web of Science and compared the duration for which the pigs were significantly sensitized as well as the intensity of mechanical sensitization. We searched models within the specific field of pain and adjacent fields in which pain induction or assessment is relevant, such as pig production. Second, we compared assessment methodologies in surrogate pain models in humans and pigs to identify areas of overlap and possible improvement. Based on the literature search, 23 types of porcine pain models were identified; 13 of which could be compared quantitatively. The induced sensitization lasted from hours to months and intensities ranged from insignificant to the maximum attainable. We also found a near to complete overlap of assessment methodologies between human and pig models within the area of peripheral neurophysiology, which allows for direct comparison of results obtained in the two species. In spite of this overlap, further development of pain assessment methodologies is still needed. We suggest that central nervous system electrophysiology, such as electroencephalography, electrocorticography or intracortical recordings, may pave the way for future objective pain assessment.