RESTORATION OF RETINAL GANGLION CELL DENDRITIC ARBORS AFTER AXONAL INJURY IN VIVO.
Bibliographic record
Abstract
Purpose Dendrites are major determinants of how retinal neurons integrate and process incoming information. We showed that retinal ganglion cells (RGCs) undergo shrinkage of dendritic arbors soon after axotomy, but the molecular mechanisms that underlie injury‐induced dendritic remodeling are poorly understood. Here, we investigated the role of the mTOR (mammalian target of rapamycin) pathway in RGC dendritic structure and synaptic input after acute optic nerve lesion. Methods Adult transgenic mice carrying the yellow fluorescent protein (YFP) gene were subjected to optic nerve axotomy. Retinal mTOR activity was manipulated by intraocular injection of siRNA against the mTOR repressor REDD2 or by intraperitoneal administration of the mTOR inhibitor rapamycin. Retinal whole‐mounts were prepared and RGC dendritic trees were 3‐D reconstructed. Results Optic nerve axotomy leads to marked downregulation of mTOR activity in RGCs correlating with dendritic shrinkage prior to RGC death onset. siREDD2 stimulated mTOR activity in injured RGCs leading to increased dendritic length, surface and complexity compared to control retinas. Importantly, increased mTOR activity in RGCs restored glutamatergic bipolar cell inputs onto RGC dendritic shafts. Conclusion We report a novel role for the mTOR pathway in the restoration of RGC dendritic arbors and excitatory synaptic input after acute optic nerve lesion.
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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) | 0.001 | 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".