Laminin enhances the growth of human neural stem cells in defined culture media
Bibliographic record
Abstract
BACKGROUND: Human neural stem cells (hNSC) have the potential to provide novel cell-based therapies for neurodegenerative conditions such as multiple sclerosis and Parkinson's disease. In order to realise this goal, protocols need to be developed that allow for large quantities of hNSC to be cultured efficiently. As such, it is important to identify factors which enhance the growth of hNSC. In vivo, stem cells reside in distinct microenvironments or niches that are responsible for the maintenance of stem cell populations. A common feature of niches is the presence of the extracellular matrix molecule, laminin. Therefore, this study investigated the effect of exogenous laminin on hNSC growth. RESULTS: To measure hNSC growth, we established culture conditions using B27-supplemented medium that enable neurospheres to grow from human neural cells plated at clonal densities. Limiting dilution assays confirmed that neurospheres were derived from single cells at these densities. Laminin was found to increase hNSC numbers as measured by this neurosphere formation. The effect of laminin was to augment the proliferation/survival of the hNSC, rather than promoting the undifferentiated state. In agreement, apoptosis was reduced in dissociated neurospheres by laminin in an integrin beta1-dependent manner. CONCLUSION: The addition of laminin to the culture medium enhances the growth of hNSC, and may therefore aid their large-scale production.
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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 | 0.000 |
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".