Laminin enhances the growth of human neural stem cells in defined culture media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it