Expansion of Human Neural Precursor Cells in Large‐Scale Bioreactors for the Treatment of Neurodegenerative Disorders
Bibliographic record
Abstract
The transplantation of in vitro expanded human neural precursor cells (hNPCs) represents a potential new treatment alternative for individuals suffering from incurable neurodegenerative disorders such as Parkinson's disease (PD) and Huntington's disease (HD). However, in order for cell restorative therapy to have widespread therapeutic significance, it will be necessary to generate unlimited quantities of clinical grade hNPCs in a standardized method. We report here that we have developed a serum-free medium and scale-up protocols that allow for the generation of clinical quantities of human telencephalon-derived hNPCs in 500-mL computer-controlled suspension bioreactors. The average hNPC aggregate diameter in the bioreactors was maintained below a target value of 500 microm by controlling the liquid shear field. The human cells, which were inoculated at 10(5) cells/mL, exhibited a doubling time of 84 h, underwent a 36-fold expansion over the course of 18 days, and maintained an average viability of over 90%. The bioreactor-derived hNPCs retained their nestin expression following expansion and were able to differentiate into glial and neuronal phenotypes under defined conditions. It has also been demonstrated that these hNPCs differentiated to a GABAergic phenotype that has recently been shown to be able to restore functional behavior in rat models of HD and neuropathic pain (Mukhida, K. et al. Stem Cells 2007; DOI 10.1634/stemcells.2007-0326). This study demonstrates that clinical quantities of hNPCs can be successfully and reproducibly generated under standardized conditions in computer-controlled 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 | 0.000 | 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".