Development and initial validation of a dog quality of life instrument.

The increasing attention for the dog-owner relationship combined with advances in nutrition and veterinary care have made wellbeing a focal point for dog owners, veterinarians, and dog product and service providers. While canine wellbeing can be quantified by survey-based quality of life instruments like those used in human healthcare, there are currently few instruments available that can do this reliably and at scale. Here we report the development and initial validation of a canine quality of life instrument specifically designed to quantify wellbeing in the general dog population. The instrument is based on a simple 32-question survey and includes 5 daytime domains (energetic, mobile, relaxed, happy, sociable) and 3 mealtime domains (relaxed, interested and satisfied). It captures specific health-related aspects as well as more general wellbeing aspects and, in an initial sample of 2813 dogs, already provides useful insights on canine wellbeing. We believe that data collection at scale with this instrument will help bring optimal wellbeing to the dogs we care for.

Evaluation of Allogeneic Bone-Marrow-Derived and Umbilical Cord Blood-Derived Mesenchymal Stem Cells to Prevent the Development of Osteoarthritis in An Equine Model.

Osteoarthritis (OA) is a significant cause of pain in both humans and horses with a high socio-economic impact. The horse is recognized as a pertinent model for human OA. In both species, regenerative therapy with allogeneic mesenchymal stem cells (MSCs) appears to be a promising treatment but, to date, no in vivo studies have attempted to compare the effects of different cell sources on the same individuals. The objective of this study is to evaluate the ability of a single blinded intra-articular injection of allogeneic bone-marrow (BM) derived MSCs and umbilical cord blood (UCB) derived MSC to limit the development of OA-associated pathological changes compared to placebo in a post-traumatic OA model applied to all four fetlock joints of eight horses. The effect of the tissue source (BM vs. UCB) is also assessed on the same individuals. Observations were carried out using clinical, radiographic, ultrasonographic, and magnetic resonance imaging methods as well as biochemical analysis of synovial fluid and postmortem microscopic and macroscopic evaluations of the joints until Week 12. A significant reduction in the progression of OA-associated changes measured with imaging techniques, especially radiography, was observed after injection of bone-marrow derived mesenchymal stem cells (BM-MSCs) compared to contralateral placebo injections. These results indicate that allogeneic BM-MSCs are a promising treatment for OA in horses and reinforce the importance of continuing research to validate these results and find innovative strategies that will optimize the therapeutic potential of these cells. However, they should be considered with caution given the low number of units per group.

An experimentally induced osteoarthritis model in horses performed on both metacarpophalangeal and metatarsophalangeal joints: Technical, clinical, imaging, biochemical, macroscopic and microscopic characterization.

Osteoarthritis is a common cause of pain and economic loss in both humans and horses. The horse is recognized as a suitable model for human osteoarthritis, because the thickness, structure, and mechanical properties of equine articular cartilage are highly comparable to those of humans. Although a number of equine experimental osteoarthritis models have been described in the literature, these cases generally involve the induction of osteoarthritis in just one joint of each animal. This approach necessitates the involvement of large numbers of horses to obtain reliable data and thus limits the use of this animal model, for both economic and ethical reasons. This study adapts an established equine model of post-traumatic osteoarthritis to induce osteoarthritis-associated lesions in all 4 fetlock joints of the same horse in order to reduce the number of animals involved and avoid individual variability, thus obtaining a more reliable method to evaluate treatment efficacy in future studies. The objectives are to assess the feasibility of the procedure, evaluate variability of the lesions according to interindividual and operated-limb position and describe the spontaneous evolution of osteoarthritis-associated pathological changes over a twelve-week period. The procedure was well tolerated by all 8 experimental horses and successfully induced mild osteoarthritis-associated changes in the four fetlock joints of each horse. Observations were carried out using clinical, radiographic, ultrasonographic, and magnetic resonance imaging methods as well as biochemical analyses of synovial fluid and postmortem microscopic and macroscopic evaluations of the joints. No significant differences were found in the progression of osteoarthritis-associated changes between horses or between the different limbs, with the exception of higher synovial effusion in hind fetlocks compared to front fetlocks and higher radiographic scores for left fetlocks compared to the right. This model thus appears to be a reliable means to evaluate the efficacy of new treatments in horses, and may be of interest for translational studies in human medicine.

A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method.

With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor conditions. The accuracy of two speed calculation methods was compared: one signal based and one machine learning model. Those two methods allowed the calculation of speed from accelerometric and gyroscopic data without any other external input. For this purpose, data were collected under various speeds on straight lines and curved paths. Two reference systems were used to measure the speed in order to have a reference speed value to compare each tested model and estimate their accuracy. Those models were compared according to three different criteria: the percentage of error above 0.6 m/s, the RMSE, and the Bland and Altman limit of agreement. The machine learning method outperformed its competitor by giving the lowest value for all three criteria. The main contribution of this work is that it is the first method that gives an accurate speed per stride for horses without being coupled with a global positioning system or a magnetometer. No similar study performed on horses exists to compare our work with, so the presented model is compared to existing models for human walking. Moreover, this tool can be extended to other equestrian sports, as well as bipedal locomotion as long as consistent data are provided to train the machine learning model. The machine learning model's accurate results can be explained by the large database built to train the model and the innovative way of slicing stride data before using them as an input for the model.