Microcarrier Screening for Skin-derived Precursor Schwann Cell Culture in Stirred Tank Bioreactors
Bibliographic record
Abstract
Skin derived precursor Schwann cells (SKP-SCs) are currently being investigated for use in peripheral nerve repair. Current static culture methods are not adequate to produce the high number of cells needed for treatments. As a result, suspension bioreactors are an attractive option. To culture adherent cells, like the SKP-SCs, in suspension, it is common practise to use small spherical beads called microcarriers. Microcarriers typically have diameters of 100µm to 400µm can be manufactured out of many materials, but are typically made from materials that can withstand the forces seen in a bioreactor. When inoculated, the cells will attach to the microcarriers and proliferate. This attachment depends on many factors including chemical composition, surface topography, degree of porosity, and charge. Because there are many different commercially available microcarriers with varying properties, we needed to screen these for our specific cell type. We selected four microcarriers to test, Cytodex 3, Hillex II, ProNectin F, and Plastic Plus. We first compared attachment to the microcarriers in shaken well plates, then compared the growth kinetics of SKP-SCs in the shaken well plates. Finally we investigated the growth kinetics of SKP-SCs in bioreactors. We found that Cytodex 3 and Hillex II had the highest attachment rate after 18 hours. Over the growth period of 9 days, Cytodex 3 showed significantly higher growth compared to the other microcarriers. Lastly Cytodex 3 had the highest growth in suspension bioreactors. Based on these results we are confident in using Cytodex 3 to develop our process further.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".