New Tissue Dissociation Protocol for Scaled-up Production of Neural Stem Cells in Suspension Bioreactors
Bibliographic record
Abstract
The successful dissociation of mammalian neural stem cell (NSC) aggregates (neurospheres) into a single-cell suspension is an important procedure when expanding NSCs for clinical use, or when performing important assays such as clonal analyses. Until now, researchers have had to rely primarily on destructive mechanical methods such as trituration with a pipette tip to break apart the aggregates. In this study we report on a new chemical dissociation procedure that is efficient, cost effective, reproducible, and much less harmful to murine NSCs than both mechanical and enzymatic techniques. This method, involving the manipulation of environmental pH levels, resulted in 40% higher measured cell densities and 15-20% higher viabilities compared with mechanical dissociation. Moreover, chemical dissociation resulted in the production of significantly less cellular debris. Chemical dissociation was found to have no adverse effects on the long-term proliferation of the NSCs, which retained the ability to proliferate, form neurospheres, self-renew, and exhibit multipotentiality. This chemical method represents a new approach for the dissociation of tissues.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".