In Vivo Imaging of Retinal Ganglion Cell Axons within the Nerve Fiber Layer
Bibliographic record
Abstract
Purpose. Optic nerve injury causes loss of retinal ganglion cells (RGCs) and their axons. The reduction in RGC counts over time in axonal injury is well studied, but the correlation with the timing of anterograde and retrograde axonal degeneration is less clear. The authors longitudinally imaged RGC axons stained with a chloromethyl derivative of fluorescein diacetate (CMFDA) in live rats after optic nerve injury. Methods. Optic nerves were transected. Three days later CMFDA was intravitreously injected. Confocal scanning laser ophthalmoscopy was performed daily, and mean fluorescence intensity and the number of CMFDA bundles were calculated. RGC soma survival was studied after retrograde fluorescence labeling. Retinal nerve fiber layer (RNFL) thickness was evaluated histologically. Results. CMFDA-positive RGC axon bundles could be imaged in vivo. Axons lost 68% +/- 29% of their fluorescence by 7 days after transection compared with 25% +/- 21% in nontransected eyes. The number of labeled axon bundles decreased by 61% +/- 28% at 7 days after transection compared with 26% +/- 9% in nontransected eyes. The number of retrograde-labeled RGCs detected in vivo declined by 53% at 7 days and by 76% at 14 days after transection. RGC soma and CMFDA axon counts decreased most rapidly between 5 and 7 days after transection. Histologic examination demonstrated a reduction in RNFL thickness 7 days after transection. Conclusions. Intravitreal CMFDA can be used to longitudinally monitor RGC axons within the RNFL in vivo. Imaging the disappearance of retrograde-labeled RGC somas and axons indicates that axonal and somal degeneration occur in parallel after axotomy.
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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".