Osteogenic potential of sorted equine mesenchymal stem cell subpopulations.
Bibliographic record
Abstract
The objectives of this study were to use non-equilibrium gravitational field-flow fractionation (GrFFF), an immunotag-less method of sorting mesenchymal stem cells (MSCs), to sort equine muscle tissue-derived mesenchymal stem cells (MMSCs) and bone marrow-derived mesenchymal stem cells (BMSC) into subpopulations and to carry out assays in order to compare their osteogenic capabilities. Cells from 1 young adult horse were isolated from left semitendinosus muscle tissue and from bone marrow aspirates of the fourth and fifth sternebrae. Aliquots of 800 × 10(3) MSCs from each tissue source were sorted into 5 fractions using non-equilibrium GrFFF (GrFFF proprietary system). Pooled fractions were cultured and expanded for use in osteogenic assays, including flow cytometry, histochemistry, bone nodule assays, and real-time quantitative polymerase chain reaction (qPCR) for gene expression of osteocalcin (OCN), RUNX2, and osterix. Equine MMSCs and BMSCs were consistently sorted into 5 fractions that remained viable for use in further osteogenic assays. Statistical analysis confirmed strongly significant upregulation of OCN, RUNX2, and osterix for the BMSC fraction 4 with P < 0.00001. Flow cytometry revealed different cell size and granularity for BMSC fraction 4 and MMSC fraction 2 compared to unsorted controls and other fractions. Histochemisty and bone nodule assays revealed positive staining nodules without differences in average nodule area, perimeter, or stain intensity between tissues or fractions. As there are different subpopulations of MSCs with different osteogenic capacities within equine muscle- and bone marrow-derived sources, these differences must be taken into account when using equine stem cell therapy to induce bone healing in 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 | 0.000 | 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".