Neural Stem Cells: Bioprocess Engineering
Bibliographic record
Abstract
Abstract The discovery of neural stem cells in the adult mammalian brain has created new hope that progressive and currently incurable neurodegenerative conditions, such as Parkinson's disease, can be effectively treated using transplantation‐based cell therapies. Unfortunately, the sparse nature of stem cells in mammalian tissues prevents them from being isolated in the quantities needed to efficiently develop such therapies. Standard culture methods combined with scalable bioprocesses to expand significantly isolated stem cell populations would allow this bottleneck to be eliminated. Moreover, access to large numbers of neural stem cells would not only enable the development of new stem cell‐based therapies, but would also subsequently facilitate the widespread clinical implementation of such treatments. In order to address the issue of stem cell scarcity, we have conducted extensive research at the Pharmaceutical Production Research Facility (PPRF) in Calgary related to the scale‐up of neural stem cell production. In this article, we present firstly the properties and applications of mammalian neural stem cells, and then review the bioprocess engineering research that has been carried out at PPRF. Our work has resulted in the successful development of a robust bioreactor technology platform to efficiently and reproducibly generate clinical quantities of mammalian neural stem cells.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".