Cell Therapy in Veterinary Medicine as a Proof-of-Concept for Human Therapies: Perspectives From the North American Veterinary Regenerative Medicine Association
Bibliographic record
Abstract
In the past decade, the potential to translate scientific discoveries in the area of regenerative therapeutics in veterinary species to novel, effective human therapies has gained interest from the scientific and public domains. Translational research using a One Health approach provides a fundamental link between basic biomedical research and medical clinical practice, with the goal of developing strategies for curing or preventing disease and ameliorating pain and suffering in companion animals and humans alike. Veterinary clinical trials in client-owned companion animals affected with naturally occurring, spontaneous disease can inform human clinical trials and significantly improve their outcomes. Innovative cell therapies are an area of rapid development that can benefit from non-traditional and clinically relevant animal models of disease. This manuscript outlines cell types and therapeutic applications that are currently being investigated in companion animals that are affected by naturally occurring diseases. We further discuss how such investigations impact translational efforts into the human medical field, including a critical evaluation of their benefits and shortcomings. Here, leaders in the field of veterinary regenerative medicine argue that experience gained through the use of cell therapies in companion animals with naturally occurring diseases represent a unique and under-utilized resource that could serve as a critical bridge between laboratory/preclinical models and successful human clinical trials through a One-Health approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".