A Multi-Stage Bioprocess for the Expansion of Rodent Skin-Derived Schwann Cells in Computer-Controlled Bioreactors
Bibliographic record
Abstract
Regenerative therapies for the treatment of peripheral nerve and spinal cord injuries can require hundreds of millions of autologous cells. Current treatments involve the harvest of Schwann cells (SCs) from nerves; however, this is an invasive procedure. Therefore, a promising alternative is using skin-derived Schwann cells (Sk-SCs), in which between 3-5 million cells can be harvested from a standard skin biopsy. However, traditional static planar culture is still inefficient at expanding cells to clinically relevant numbers. As a result, bioreactors can be used to develop reproducible bioprocesses for the large-scale expansion of therapeutic cells. Here, we present a proof-of-concept SC manufacturing bioprocess using rat Sk-SCs. With this integrated process, we were able to simulate a feasible bioprocess, taking into consideration the harvest and shipment of cells to a production facility, the generation of the final cell product, and the cryopreservation and shipment of cells back to the clinic and patient. This process started with 3 million cells and inoculated and expanded them to over 200 million cells in 6 days. Following the harvest and post-harvest cryopreservation and thaw, we were able to maintain 150 million viable cells that exhibited a characteristic Schwann cell phenotype throughout each step of the process. This process led to a 50-fold expansion, producing a clinically relevant number of cells in a 500 mL bioreactor in just 1 week, which is a dramatic improvement over current methods of expansion.
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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 | 0.000 | 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".