Direct reprogramming of astrocytes to neurons leads to functional recovery after stroke
Bibliographic record
Abstract
Abstract Stroke is the leading cause of adult disability with few treatment options for stroke survivors. Astrocyte reprogramming to neurons enables the targeted in vivo generation of new cells at the site of injury and represents a novel approach for brain repair. A number of studies have demonstrated successful conversion of astrocytes to neurons in various models of brain injury and disease; however, the impact of this strategy on tissue and functional outcome following stroke is not well established. Using AAV delivery of the transcription factor NeuroD1, we reprogrammed astrocytes 7 days after endothelin-1 induced cortical stroke, and studied the long-term cellular and functional outcomes. We found that by 63 days post-stroke, 20% of neurons in the perilesional cortex were reprogrammed. Furthermore, reprogrammed neurons had matured into regionally appropriate neuronal subtypes. Importantly, this treatment was associated with improved functional outcome using the foot fault test and gait analysis. Together, our findings indicate that in vivo reprogramming is a promising regenerative approach for stroke repair.
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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".