Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation.

BACKGROUND: Among the 6-8 million animals that enter the rescue shelters every year, nearly 3-4 million (i.e., 50% of the incoming animals) are euthanized, and 10-25% of them are put to death specifically because of shelter overcrowding each year. The overall goal of this study is to increase the adoption rates at animal shelters. This involves predicting the length of stay of each animal at shelters considering key features such as animal type (dog, cat, etc.), age, gender, breed, animal size, and shelter location. RESULTS: Logistic regression, artificial neural network, gradient boosting, and the random forest algorithms were used to develop models to predict the length of stay. The performance of these models was determined using three performance metrics: precision, recall, and F1 score. The results demonstrated that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Upon further observation of the results, it was found that age for dogs (puppy, super senior), multicolor, and large and small size were important predictor variables. CONCLUSION: The findings from this study can be utilized to predict and minimize the animal length of stay in a shelter and euthanization. Future studies involve determining which shelter location will most likely lead to the adoption of that animal. The proposed two-phased tool can be used by rescue shelters to achieve the best compromise solution by making a tradeoff between the adoption speed and relocation cost.

Development and Characterization of a AuNP-Genipin-Viscoelastic Collagen Material.

Lower back pain is one of the leading causes of disability, affecting 11.9% of the population worldwide and studies have shown that intervertebral disc degeneration is a common cause for chronic lower back pain. We have explored the combination of three components, viscoelastic collagen, genipin, and gold nanoparticles to determine its potential to promote regeneration of the intervertebral disc, specifically for nucleus pulposus regeneration. The goal of this study was to develop, fabricate and characterize different formulations of viscoelastic collagen conjugated with gold nanoparticles and genipin to assess the feasibility as a tissue template. Results demonstrated the successful attachment of gold nanoparticles to the viscoelastic collagen utilizing the genipin crosslinker. For each of the viscoelastic collagen compositions examined, cell biocompatibility was achieved. The results also demonstrated an increase in stiffness of the material with different sizes and concentrations of AuNPs. Results from the TEM and STEM also demonstrated that the viscoelastic collagen that was developed did not display the characteristic D banding pattern of polymerized collagen. The findings from this study could lead to the development of a more efficient and cost-effective treatment for patients with chronic back pain caused by IVD degeneration.