Bioprocess development for cord blood mesenchymal stromal cells on microcarriers in Vertical‐Wheel bioreactors
Bibliographic record
Abstract
Equine mesenchymal stromal cells (MSCs) have been found to be beneficial for the treatment of many ailments, including orthopedic injuries, due to their superior differentiation potential and immunomodulating properties. Cell therapies require large cell numbers, which are not efficiently generated using conventional static expansion methods. Expansion of equine cord blood-derived MSCs (eCB-MSCs) in bioreactors, using microcarriers as an attachment surface, has the potential to generate large numbers of cells with increased reproducibility and homogeneity compared with static T-flask expansion. This study investigated the development of an expansion process using Vertical-Wheel (VW) bioreactors, a single-use bioreactor technology that incorporates a wheel instead of an impeller. Initially, microcarriers were screened at small scale to assess eCB-MSC attachment and growth and then in bioreactors to assess cell expansion and harvesting. The effect of different donors, serial passaging, and batch versus fed batch were all examined in 0.1 L VW bioreactors. The use of VW bioreactors with an appropriate microcarrier was shown to be able to produce cell densities of up to 1E6 cells/mL, while maintaining cell phenotype and functionality, thus demonstrating great potential for the use of these bioreactors to produce large cell numbers for cell 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 | 0.001 |
| 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".