Serum-free bioprocessing of adult human and rodent skin-derived Schwann cells: implications for cell therapy in nervous system injury
Bibliographic record
Abstract
Peripheral nerve injury affects 2.8% of trauma patients with severe cases often resulting in long-lived permanent disability, despite nerve repair surgery. Autologous Schwann cell (SC) therapy currently provides an exciting avenue for improved outcomes for these patients, particularly with the possibility to derive SCs from easily-accessible adult skin. However, due to current challenges regarding the efficient expansion of these cells, further optimization is required before they can be seriously considered for clinical application. Here, a microcarrier-based bioreactor system is proposed as a means to scale-up large numbers of adult skin-derived SCs for transplantation into the injured nerve. Bioprocessing parameters that allow for the expansion of adult rodent SCs have been identified, whilst maintaining similar rates of proliferation (as compared to static-grown SCs), expression of SC markers, and, importantly, their capacity to myelinate axons following transplant into the injured sciatic nerve. The same bioprocessing parameters can be applied to SCs derived from adult human skin, and like rodent cells, they sustain their proliferative potential and expression of SC markers. Taken together, this dataset demonstrates the basis for a scalable bioprocess for the production of SCs, an important step towards clinical use of these cells as an adjunct therapy for nerve repair. Copyright © 2017 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".