Canine Mesenchymal Stem Cell Potential and the Importance of Dog Breed: Implication for Cell-Based Therapies
Bibliographic record
Abstract
The study of canine bone marrow-derived mesenchymal stem cells (MSCs) has a prominent position in veterinary cell-based applications. Yet the plethora of breeds, their different life spans, and interbreed variations provide unclearness on what can be achieved specifically by such therapies. In this study, we compared a set of morphological, physiological, and genetic markers of MSCs derived from large dog breeds, namely, Border collie, German shepherd, Labrador, Malinois, Golden retriever, and Hovawart. We compared colony-forming units (CFUs) assay, population doubling time (PDT), senescence-associated β-galactosidase (SA-β-gal) activity, telomere length, and gene expression of MSCs, as well as the ability of cells to differentiate to osteogenic, adipogenic, and chondrogenic phenotypes. The influence of the culture media α-MEM, low-glucose DMEM, and high-glucose DMEM, used in cell isolation and expansion, was investigated in the presence and absence of basic fibroblast growth factor (bFGF). Initial cell yield was not affected by culturing medium, but MSCs expanded best in α-MEM supplemented with bFGF. After isolation, the number of MSCs was similar among breeds--as shown by equivalent CFUs--except in the Hovawart samples, which had fivefold less CFU. Telomere lengths were similar among breeds. MSCs divided actively only for 4 weeks in culture (PDT = ∼50 h/division), except Border collie cells divided for a longer time than cells from other groups. The percentage of senescent cells increased linearly in all breeds with time, with a faster rate in German shepherd, Labrador, and Golden retriever. Border collie cells underwent efficient osteogenic differentiation, Hovawart cells performed the best in chondrogenic differentiation, and Labrador cells in both, while German shepherd cells had the lower differentiation potential. MSCs from all breeds preserved the same adipogenic differentiation potential. In conclusion, despite variations, isolated MSCs can be expanded and differentiated in vitro, and all breeds are eligible for MSC-based therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".