Vascular Endothelial Growth Factor Directly Inhibits Primitive Neural Stem Cell Survival But Promotes Definitive Neural Stem Cell Survival
Bibliographic record
Abstract
There are two types of neural stem cells (NSCs). Primitive NSCs [leukemia inhibitory factor (LIF) dependent but exogenous fibroblast growth factor (FGF) 2 independent] can be derived from mouse embryonic stem (ES) cells in vitro and from embryonic day 5.5 (E5.5) to E7.5 epiblast and E7.5-E8.5 neuroectoderm in vivo. Definitive NSCs (LIF independent but FGF2 dependent) first appear in the E8.5 neural plate and persist throughout life. Primitive NSCs give rise to definitive NSCs. Loss and gain of functions were used to study the role of vascular endothelial growth factor (VEGF)-A and its receptor, Flk1, in NSCs. The numbers of Flk1 knock-out mice embryo-derived and ES cell-derived primitive NSCs were increased because of the enhanced survival of primitive NSCs. In contrast, neural precursor-specific, Flk1 conditional knock-out mice-derived, definitive NSCs numbers were decreased because of the enhanced cell death of definitive NSCs. These effects were not observed in cells lacking Flt1, another VEGF receptor. In addition, the cell death stimulated by VEGF-A of primitive NSC and the cell survival stimulated by VEGF-A of definitive NSC were blocked by Flk1/Fc-soluble receptors and VEGF-A function-blocking antibodies. These VEGF-A phenotypes also were blocked by inhibition of the downstream effector nuclear factor kappaB (NF-kappaB). Thus, both the cell death of primitive NSC and the cell survival of definitive NSC induced by VEGF-A stimulation are mediated by bifunctional NF-kappaB effects. In conclusion, VEGF-A function through Flk1 mediates survival (and not proliferative or fate change) effects on NSCs, specifically.
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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 | 0.001 |
| 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".