Regeneration of Inner Retinal Neurons after Intravitreal Injection of Ouabain in Zebrafish
Bibliographic record
Abstract
We examined the regenerative capacity of the adult zebrafish retina by intravitreal injection of a low ouabain concentration to rapidly damage the ganglion cell layer (GCL) and inner nuclear layer (INL) with minimal photoreceptor cell damage. By 24 h after ouabain injection, maximal numbers of terminal deoxynucleotidyl transferase-mediated biotinylated UTP nick end labeling (TUNEL)-positive cells were detected in the INL and GCL, with low numbers of TUNEL-positive cells in the outer nuclear layer. Immunolabeling revealed that approximately 85% of the HuC/D-positive amacrine and ganglion cells were lost by 7 d post-ouabain injection (dpi). This ganglion cell loss was consistent with the small, but statistically significant, decrease in the optic nerve diameter. The regeneration response began within 1 dpi with increased proliferating cell nuclear antigen (PCNA) expression in both the INL and GCL. By 3 dpi, PCNA expression is primarily restricted to the Müller glia. By 5 dpi, most of the PCNA expression was localized to neuronal progenitors expressing the olig2:egfp transgene rather than the Müller glia. By 7 dpi, the neuronal progenitors began committing to the ganglion cell fate based on the coexpression of the atoh7:EGFP transgene and the zn5 antigen. The regeneration of ganglion and amacrine cells continued until 60 dpi, when they reached 75% of their uninjected control number. This demonstrates that inner retinal damage, without extensive photoreceptor damage, is sufficient to induce a regeneration response that is marked by the Müller glial cells reentering the cell cycle to produce neuronal progenitor cells that regenerate INL and ganglion cells in the zebrafish retina.
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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".