Trk signaling regulates neural precursor cell proliferation and differentiation during cortical development
Bibliographic record
Abstract
Increasing evidence indicates that development of embryonic central nervous system precursors is tightly regulated by extrinsic cues located in the local environment. Here, we asked whether neurotrophin-mediated signaling through Trk tyrosine kinase receptors is important for embryonic cortical precursor cell development. These studies demonstrate that inhibition of TrkB (Ntrk2) and/or TrkC (Ntrk3) signaling using dominant-negative Trk receptors, or genetic knockdown of TrkB using shRNA, caused a decrease in embryonic precursor cell proliferation both in culture and in vivo. Inhibition of TrkB/C also caused a delay in the generation of neurons, but not astrocytes, and ultimately perturbed the postnatal localization of cortical neurons in vivo. Conversely, overexpression of BDNF in cortical precursors in vivo promoted proliferation and enhanced neurogenesis. Together, these results indicate that neurotrophin-mediated Trk signaling plays an essential, cell-autonomous role in regulating the proliferation and differentiation of embryonic cortical precursors and thus controls cortical development at earlier stages than previously thought.
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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".