Application of a novel sorting system for equine mesenchymal stem cells (MSCs).
Bibliographic record
Abstract
The objective of this study was to validate non-equilibrium gravitational field-flow fractionation (GrFFF), an immunotag-less method of sorting mesenchymal stem cells (MSCs) into subpopulations, for use with MSCs derived from equine muscle tissue, periosteal tissue, bone marrow, and adipose tissue. Cells were collected from 6 young, adult horses, postmortem. Cells were isolated from left semitendinosus muscle tissue, periosteal tissue from the distomedial aspect of the right tibia, bone marrow aspirates from the fourth and fifth sternebrae, and left supragluteal subcutaneous adipose tissue. Aliquots of 800 × 10(3) MSCs from each tissue source were separated and injected into a ribbon-like capillary device by continuous flow (GrFFF proprietary system). Cells were sorted into 6 fractions and absorbencies [optical density (OD)] were read. Six fractions from each of the 6 aliquots were then combined to provide pooled fractions that had adequate cell numbers to seed at equal concentrations into assays. Equine muscle tissue-derived, periosteal tissue-derived, bone marrow-derived, and adipose tissue-derived mesenchymal stem cells were consistently sorted into 6 fractions that remained viable for use in further assays. Fraction 1 had more cuboidal morphology in culture when compared to the other fractions. Statistical analysis of the fraction absorbencies (OD) revealed a P-value of < 0.05 when fractions 2 and 3 were compared to fractions 1, 4, 5, and 6. It was concluded that non-equilibrium GrFFF is a valid method for sorting equine muscle tissue-derived, periosteal tissue-derived, bone marrow-derived, and adipose tissue-derived mesenchymal stem cells into subpopulations that remain viable, thus securing its potential for use in equine stem cell applications and veterinary medicine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".