Isolation, characterization, and in vitro proliferation of canine mesenchymal stem cells derived from bone marrow, adipose tissue, muscle, and periosteum
Bibliographic record
Abstract
OBJECTIVE: To isolate and characterize mesenchymal stem cells (MSCs) from canine muscle and periosteum and compare proliferative capacities of bone marrow-, adipose tissue-, muscle-, and periosteum-derived MSCs (BMSCs, AMSCs, MMSCs, and PMSCs, respectively). SAMPLE: -7 canine cadavers. PROCEDURES: -MSCs were characterized on the basis of morphology, immunofluorescence of MSC-associated cell surface markers, and expression of pluripotency-associated transcription factors. Morphological and histochemical methods were used to evaluate differentiation of MSCs cultured in adipogenic, osteogenic, and chondrogenic media. Messenger ribonucleic acid expression of alkaline phosphatase, RUNX2, OSTERIX, and OSTEOPONTIN were evaluated as markers for osteogenic differentiation. Passage-1 MSCs were counted at 24, 48, 72, and 96 hours to determine tissue-specific MSC proliferative capacity. Mesenchymal stem cell yield per gram of tissue was calculated for confluent passage-1 MSCs. RESULTS: -Successful isolation of BMSCs, AMSCs, MMSCs, and PMSCs was determined on the basis of morphology; expression of CD44 and CD90; no expression of CD34 and CD45; mRNA expression of SOX2, OCT4, and NANOG; and adipogenic and osteogenic differentiation. Proliferative capacity was not significantly different among BMSCs, AMSCs, MMSCs, and PMSCs over a 4-day culture period. Periosteum provided a significantly higher MSC yield per gram of tissue once confluent in passage 1 (mean ± SD of 19,400,000 ± 12,800,000 of PMSCs/g of periosteum obtained in a mean ± SD of 13 ± 1.64 days). CONCLUSIONS AND CLINICAL RELEVANCE: -Results indicated that canine muscle and periosteum may be sources of MSCs. Periosteum was a superior tissue source for MSC yield and may be useful in allogenic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".