Bioengineering Protocols for Neural Precursor Cell Expansion
Bibliographic record
Abstract
Neural precursor cells (NPCs) isolated from different regions of the developing or adult CNS may represent a new source of cells that may have utility in future cell replacement therapies aimed at treating neurodegenerative disorders. Moreover, these cells may have applications in a number of non-clinical areas from basic biological research to gene and drug delivery. However, their sparse concentration within the CNS means that they can only be isolated in small quantities, and thus need to be expanded to numbers that are sufficient for clinical and non-clinical applications. This chapter discusses the cell-handling protocols relevant to the expansion of murine NPCs and human NPCs in both standard tissue culture flasks (as effective means for handling the small quantities of stem cells following isolation from primary tissue) and suspension bioreactors (as highly efficient mode of culture for production of large number of cells in a standardized, controlled manner). Considering that mammalian NPCs can grow under serum-free conditions as neurospheres, or adhere to the culture flask surface, passaging protocols concerning aggregate dissociation techniques, cell sampling and inoculation procedures are described for both floating aggregates of cells and adherent cultures. Special attention is paid to important design considerations (i.e., mass transfer and culture hydrodynamics) as well as process control techniques as crucial parameters to achieve a reproducible and productive cell expansion process in suspension bioreactors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.018 |
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".