Osteoarthritis (OA) is unquestionably one of the most important chronic health issues in humans, affecting millions of individuals and costing billions of dollars annually. Despite widespread awareness of this disease and its devastating impact, the pathogenesis of early OA is not completely understood, hampering the development of effective tools for early diagnosis and disease-modifying therapeutics. Most human tissue available for study is obtained at the time of joint replacement, when OA lesions are end stage and little can be concluded about the factors that played a role in disease development. To overcome this limitation, over the past 50 years, numerous induced and spontaneous animal models have been utilized to study disease onset and progression, as well as to test novel therapeutic interventions. Reflecting the heterogeneity of OA itself, no single “gold standard” animal model for OA exists; thus, a challenge for researchers lies in selecting the most appropriate model to answer a particular scientific question of interest. This review provides general considerations for model selection, as well as important features of species such as mouse, rat, guinea pig, sheep, goat, and horse, which researchers should be mindful of when choosing the “best” animal model for their intended purpose. Special consideration is given to key variations in pathology among species as well as recommended guidelines for reporting the histologic features of each model.

The prevalence of orthopedic implants is increasing with an aging population. These patients are vulnerable to risks from periprosthetic infections and instrument failures. Here, we present a dual-functional smart polymer foil coating compatible with commercial orthopedic implants to address both septic and aseptic failures. Its outer surface features optimum bioinspired mechano-bactericidal nanostructures, capable of killing a wide spectrum of attached pathogens through a physical process to reduce the risk of bacterial infection, without directly releasing any chemicals or harming mammalian cells. On its inner surface in contact with the implant, an array of strain gauges with multiplexing transistors, built on single-crystalline silicon nanomembranes, is incorporated to map the strain experienced by the implant with high sensitivity and spatial resolution, providing information about bone-implant biomechanics for early diagnosis to minimize the probability of catastrophic instrument failures. Their multimodal functionalities, performance, biocompatibility, and stability are authenticated in sheep posterolateral fusion model and rodent implant infection model.

Osteochondrosis (OC), simply defined as a failure of endochondral ossification, is a complex disease with both genetic and environmental risk factors that is commonly diagnosed in young horses, as well as other domestic species. Although up to 50 % of the risk for developing OC is reportedly inherited, specific genes and alleles underlying risk are thus far completely unknown. Regions of the genome identified as associated with OC vary across studies in different populations of horses. In this study, we used a cohort of Standardbred horses from the U.S. (n = 182) specifically selected for a shared early environment (to reduce confounding factors) to identify regions of the genome associated with tarsal OC. Subsequently, putative risk variants within these regions were evaluated in both the discovery population and an independently sampled validation population of Norwegian Standardbreds (n = 139) with tarsal OC. After genome-wide association analysis of imputed data with information from >200,000 single nucleotide polymorphisms, two regions on equine chromosome 14 were associated with OC in the discovery cohort. Variant discovery in these and 30 additional regions of interest (including 11 from other published studies) was performed via whole-genome sequencing. 240 putative risk variants from 10 chromosomes were subsequently genotyped in both the discovery and validation cohorts. After correction for population structure, gait (trot or pace) and sex, the variants most highly associated with OC status in both populations were located within the chromosome 14 regions of association. The association of putative risk alleles from within the same regions with disease status in two independent populations of Standardbreds suggest that these are true risk loci in this breed, although population-specific risk factors may still exist. Evaluation of these loci in other populations will help determine if they are specific to the Standardbred breed, or to tarsal OC or are universal risk loci for OC. Further work is needed to identify the specific variants underlying OC risk within these loci. This is the first step towards the long-term goal of constructing a genetic risk model for OC that allows for genetic testing and quantification of risk in individuals.

Several horse breeds have been specifically selected for the ability to exhibit alternative patterns of locomotion, or gaits. A premature stop codon in the gene DMRT3 is permissive for “gaitedness” across breeds. However, this mutation is nearly fixed in both American Standardbred trotters and pacers, which perform a diagonal and lateral gait, respectively, during harness racing. This suggests that modifying alleles must influence the preferred gait at racing speeds in these populations. A genome-wide association analysis for the ability to pace was performed in 542 Standardbred horses (n = 176 pacers, n = 366 trotters) with genotype data imputed to ~74,000 single nucleotide polymorphisms (SNPs). Nineteen SNPs on nine chromosomes (ECA1, 2, 6, 9, 17, 19, 23, 25, 31) reached genome-wide significance (p < 1.44 x 10⁻⁶). Variant discovery in regions of interest was carried out via whole-genome sequencing. A set of 303 variants from 22 chromosomes with putative modifying effects on gait was genotyped in 659 Standardbreds (n = 231 pacers, n = 428 trotters) using a high-throughput assay. Random forest classification analysis resulted in an out-of-box error rate of 0.61%. A conditional inference tree algorithm containing seven SNPs predicted status as a pacer or trotter with 99.1% accuracy and subsequently performed with 99.4% accuracy in an independently sampled population of 166 Standardbreds (n = 83 pacers, n = 83 trotters). This highly accurate algorithm could be used by owners/trainers to identify Standardbred horses with the potential to race as pacers or as trotters, according to the genotype identified, prior to initiating training and would enable fine-tuning of breeding programs with designed matings. Additional work is needed to determine both the algorithm’s utility in other gaited breeds and whether any of the predictive SNPs play a physiologically functional role in the tendency to pace or tag true functional alleles. Certain horse breeds have been developed over generations specifically for the ability to perform alternative patterns of movement, or gaits. Current understanding of the genetic basis for these gaits is limited to one known mutation apparently necessary, but not sufficient, for explaining variability in “gaitedness.” The Standardbred breed includes two distinct groups, trotters, which exhibit a two-beat gait in which the opposite forelimb and hind limb move together, and pacers, which exhibit an alternative two-beat gait where the legs on the same side of the body move together. Our long-term objective is to identify variants underlying the ability of certain Standardbreds to pace. In this study, we were able to identify several regions of the genome highly associated with pacing and, within these regions, a number of specific highly associated variants. Although the biological function of these variants has yet to be determined, we developed a model based on seven variants that was > 99% accurate in predicting whether an individual was a pacer or a trotter in two independent populations. This predictive model can be used by horse owners to make breeding and training decisions related to this economically important trait, and by scientists interested in understanding the biology of coordinated gait development.

Objective: To evaluate previously published predictive survival models in a population of horses undergoing colic surgery in the midwestern United States.Study design: Retrospective cohort study; single referral hospital.Animals: A total of 260 horses met the inclusion criteria.Methods: Medical records of horses undergoing surgical treatment for colic were reviewed. Previously published models were applied to cohort data to predict outcome. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for prediction of short‐term survival were calculated.Results: Single‐variable and multivariable models performed similarly for prediction of survival, with a mean 79% sensitivity (range: 44%–94%), 48% specificity (range: 22%–83%), 63% PPV (range: 56%–72%), 73% NPV (range: 60%–83%), and 64% accuracy (range: 59%–72%). Blood lactate ≤6 mmol/l and the colic severity score (CSS) were highly sensitive for prediction of survival; however, both had poor specificity.Conclusion: Single‐variable and multivariable predictive models did not perform as well for prediction of survival in the study cohort compared to original reports, suggesting that population‐specific factors contribute to patient survival.Clinical significance: Predictive models of survival developed in one population may be less reliable when used to predict outcome in horses undergoing colic surgery from an independent population. Additional model testing and refinement using data from multiple surgical centers could be considered to improve prediction of outcome for horses undergoing laparotomy for treatment of colic.