Targeted activation of primitive neural stem cells in the mouse brain
Bibliographic record
Abstract
Primitive neural stem cells (pNSCs) are the earliest NSCs to appear in the developing forebrain. They persist into the adult forebrain where they can generate all cells in the neural lineage and therefore hold great potential for brain regeneration. Thus, pNSCs are an ideal population to target to promote endogenous NSC activation. pNSCs can be isolated from the periventricular region as leukaemia inhibitory factor-responsive cells, and comprise a rare population in the adult mouse brain. We hypothesized that the pup periventricular region gives rise to more clonal pNSC-derived neurospheres but that pup-derived pNSCs are otherwise comparable to adult-derived pNSCs, and can be used to identify selective markers and activators of endogenous pNSCs. We tested the self-renewal ability, differentiation capacity and gene expression profile of pup-derived pNSCs and found them each to be comparable to adult-derived pNSCs, including being GFAP(-) , nestin(mid) , Oct4(+) . Next, we used pup pNSCs to test pharmacological compounds to activate pNSCs to promote endogenous brain repair. We hypothesized that pNSCs could be activated by targeting the cell surface proteins C-Kit and ErbB2, which were enriched in pNSCs relative to definitive NSCs (dNSCs) in an in vitro screen. C-Kit and ErbB2 signalling inhibition had distinct effects on pNSCs and dNSCs in vitro, and when infused directly into the adult brain in vivo. Targeted activation of pNSCs with C-Kit and ErbB2 modulation is a valuable strategy to activate the earliest cell in the neural lineage to contribute to endogenous brain regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".