Subventricular zone cytogenesis provides trophic support for neural repair
Bibliographic record
Abstract
Abstract Stroke enhances proliferation of neural precursor cells within the subventricular zone (SVZ) and induces ectopic migration of newborn cells towards the site of injury. Here we characterize the identity of cells arising from the SVZ after stroke and provide insight into their function by uncovering a mechanism through which they facilitate neural repair and functional recovery. Using genetic lineage tracing, we show that SVZ-derived cells that migrate towards stroke- induced cortical lesions in mice are predominantly undifferentiated precursors, suggesting that the main function of post-injury cytogenesis is not cell replacement. We find that SVZ-derived cells are a unique cellular source of trophic factors that instruct neural repair. Chemogenetic ablation of neural precursor cells or conditional knockout of VEGF in the adult neural stem cell lineage impairs neuronal and vascular reparative responses and worsens functional recovery after stroke. In addition, normal aging markedly diminishes the cytogenic response to stroke, resulting in worse functional recovery. Therapeutic replacement of VEGF in peri-infarct cortex is sufficient to induce neural repair and functional recovery in mice with arrested cytogenesis. These findings indicate that the SVZ cytogenic response following brain injury is a source of trophic support that drives neural repair and recovery.
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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) | 0.002 | 0.001 |
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".