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Development, implementation, and evaluation of a novel multidisciplinary one health course for university undergraduates

Today’s collaborations across fields of health and wellness are insufficient to meet societies’ challenges in combating disease and maintaining the ecosystem and public health. In this article, we present a One Health curriculum model designed to encourage undergraduate students of varying disciplines to value the connectedness of animals, humans, and the environment and to think innovatively about solutions to priority global health issues.

The Role of Bcl-xL Protein Research in Veterinary Oncology

Abstract: Due to their significant impact on human and animal health, cancer diseases are an area of considerable concern for both human and veterinary medicine. Research on the cancer pathogenesis in companion animals, such as dogs, allows not only for improving canine cancer treatment, but also for translating the results into human oncology.

Concise Review: Stem Cell Trials Using Companion Animal Disease Models

Studies to evaluate the therapeutic potential of stem cells in humans would benefit from more realistic animal models. In veterinary medicine, companion animals naturally develop many diseases that resemble human conditions, therefore, representing a novel source of preclinical models.

Optimization of Electronic Medical Records for Data Mining Using a Common Data Model

The increasing use of electronic health records (EHRs) in veterinary medicine creates an opportunity to utilize the high volume of electronic patient data for mining and data-driven analytics with the goal of improving patient care and outcomes. A central focus of the Clinical and Translational Science Award One Health Alliance (COHA) is to integrate efforts across multiple disciplines to better understand diseases in animals and people.

TRANSLATOR Database – A Vision for a Multi-Institutional Research Network

The formation of the CTSI One Health Alliance (COHA) network has generated the infrastructure necessary to support “Big Data” collaborative comparative and translational research in veterinary medicine. We describe the first step in the design, implementation, and dissemination of a collaborative information technology infrastructure that will serve the public and clinicians (COHA public/member based web site at and its research focused COHA Research Workbench application.

Veterinary and Human Biobanking Practices: Enhancing Molecular Sample Integrity

Animal models have historically informed veterinary and human pathophysiology. Next-generation genomic sequencing and molecular analyses using analytes derived from tissue require integrative approaches to determine macroanalyte integrity as well as morphology for imaging algorithms that can extend translational applications.

Targeting Translational Successes through CANSORT-SCI: Using Pet Dogs To Identify Effective Treatments for Spinal Cord Injury

Translation of therapeutic interventions for spinal cord injury (SCI) from laboratory to clinic has been historically challenging, highlighting the need for robust models of injury that more closely mirror the human condition. The high prevalence of acute, naturally occurring SCI in pet dogs provides a unique opportunity to evaluate expeditiously promising interventions in a population of animals that receive diagnoses and treatment clinically in a manner similar to persons with SCI, while adhering to National Institutes of Health guidelines for scientific rigor and transparent reporting.

Development of an International Canine Spinal Cord Injury (CSCI) observational registry: a collaborative data-sharing network to optimize translational studies of SCI

The canine spontaneous model of spinal cord injury (SCI) is as an important pre-clinical platform as it recapitulates key facets of human injury in a naturally occurring context. The establishment of an observational canine SCI registry constitutes a key step in performing epidemiologic studies and assessing the impact of therapeutic strategies to enhance translational research.