Activated Neural Stem Cells Contribute to Stroke-Induced Neurogenesis and Neuroblast Migration toward the Infarct Boundary in Adult Rats
Bibliographic record
Abstract
Stroke increases neurogenesis. The authors investigated whether neural stem cells or progenitor cells in the adult subventricular zone (SVZ) of rats contribute to stroke-induced increase in neurogenesis. After induction of stroke in rats, the numbers of cells immunoreactive to doublecortin, a marker for immature neurons, increased in the ipsilateral SVZ and striatum. Infusion of an antimitotic agent (cytosine-beta-D-arabiofuranoside, Ara-C) onto the ipsilateral cortex eliminated more than 98% of actively proliferating cells in the SVZ and doublecortin-positive cells in the ipsilateral striatum. However, doublecortin-positive cells rapidly replenished after antimitotic agent depletion of actively proliferating cells. Depleting the numbers of actively proliferating cells in vivo had no effect on the numbers of neurospheres formed in vitro, yet the numbers of neurospheres derived from stroke rats significantly (P<0.05) increased. Neurospheres derived from stroke rats self-renewed and differentiated into neurons and glia. In addition, doublecortin-positive cells generated in the SVZ migrated in a chainlike structure toward ischemic striatum. These findings indicate that in the adult stroke brain, increases in recruitment of neural stem cells contribute to stroke-induced neurogenesis, and that newly generated neurons migrate from the SVZ to the ischemic striatum.
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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) | 0.001 | 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) | 0.001 | 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".