Translating stem cell therapies: The role of companion animals in regenerative medicine
Bibliographic record
Abstract
Veterinarians and veterinary medicine have been integral to the development of stem cell therapies. The contributions of large animal experimental models to the development and refinement of modern hematopoietic stem cell transplantation were noted nearly five decades ago. More recent advances in adult stem cell/regenerative cell therapies continue to expand knowledge of the basic biology and clinical applications of stem cells. A relatively liberal legal and ethical regulation of stem cell research in veterinary medicine has facilitated the development and in some instances clinical translation of a variety of cell-based therapies involving hematopoietic stem cells and mesenchymal stem cells, as well as other adult regenerative cells and recently embryonic stem cells and induced pluripotent stem cells. In fact, many of the pioneering developments in these fields of stem cell research have been achieved through collaborations of veterinary and human scientists. This review aims to provide an overview of the contribution of large animal veterinary models in advancing stem cell therapies for both human and clinical veterinary applications. Moreover, in the context of the "One Health Initiative," the role veterinary patients may play in the future evolution of stem cell therapies for both human and animal patients will be explored.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".