Bioreactor expansion of human neural precursor cells in serum‐free media retains neurogenic potential
Bibliographic record
Abstract
Human neural precursor cells (hNPCs), harvested from somatic tissue and grown in vitro, may serve as a source of cells for cell replacement strategies aimed at treating neurodegenerative disorders such as Parkinson's disease (PD), Huntington's disease (HD), and intractable spinal cord pain. A crucial element in a robust clinical production method for hNPCs is a serum-free growth medium that can support the rapid expansion of cells while retaining their multipotency. Here, we report the development of a cell growth medium (PPRF-h2) for the expansion of hNPCs, achieving an overall cell-fold expansion of 10(13) over a period of 140 days in stationary culture which is significantly greater than other literature results. More importantly, hNPC expansion could be scaled-up from stationary culture to suspension bioreactors using this medium. Serial subculturing of the cells in suspension bioreactors resulted in an overall cell-fold expansion of 7.8 x 10(13) after 140 days. These expanded cells maintained their multipotency including the capacity to generate large numbers of neurons (about 60%). In view of our previous studies regarding successful transplantation of the bioreactor-expanded hNPCs in animal models of neurological disorders, these results have demonstrated that PPRF-h2 (containing dehydroepiandrosterone, basic fibroblast growth factor and human leukemia inhibitory factor) can successfully facilitate the production of large quantities of hNPCs with potential to be used in the treatment of neurodegenerative disorders.
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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".