Improved expansion of equine cord blood derived mesenchymal stromal cells by using microcarriers in stirred suspension bioreactors
Bibliographic record
Abstract
Equine mesenchymal stromal cells (MSCs) are increasingly investigated for their clinical therapeutic utility. Such cell-based treatments can require cell numbers in the millions or billions, with conventional expansion methods using static T-flasks typically inefficient in achieving these cell numbers. Equine cord blood-derived MSCs (eCB-MSCs), are promising cell candidates owing to their capacity for chondrogenic differentiation and immunomodulation. Expansion of eCB-MSCs in stirred suspension bioreactors with microcarriers as an attachment surface has the potential to generate clinically relevant numbers of cells while decreasing cost, time and labour requirements and increasing reproducibility and yield when compared to static expansion. As eCB-MSCs have not yet been expanded in stirred suspension bioreactors, a robust protocol was required to expand these cells using this method. This study outlines the development of an expansion bioprocess, detailing the inoculation phase, expansion phase, and harvesting phase, followed by phenotypic and trilineage differentiation characterization of two eCB-MSC donors. The process achieved maximum cell densities up to 75,000 cells/cm 2 corresponding to 40 million cells in a 100 mL bioreactor, with a harvesting efficiency of up to 80%, corresponding to a yield of 32 million cells from a 100 mL bioreactor. When compared to cells grown in static T-flasks, bioreactor-expanded eCB-MSC cultures did not change in surface marker expression or trilineage differentiation capacity. This indicates that the bioreactor expansion process yields large quantities of eCB-MSCs with similar characteristics to conventionally grown eCB-MSCs.
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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) | 0.001 | 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 | 0.001 |
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".