Longitudinal In Vivo Imaging of Retinal Ganglion Cells and Retinal Thickness Changes Following Optic Nerve Injury in Mice
Bibliographic record
Abstract
BACKGROUND: Retinal ganglion cells (RGCs) die in sight-threatening eye diseases. Imaging RGCs in humans is not currently possible and proof of principle in experimental models is fundamental for future development. Our objective was to quantify RGC density and retinal thickness following optic nerve transection in transgenic mice expressing cyan fluorescent protein (CFP) under control of the Thy1 promoter, expressed by RGCs and other neurons. METHODOLOGY/PRINCIPAL FINDINGS: A modified confocal scanning laser ophthalmoscopy (CSLO)/spectral-domain optical coherence tomography (SD-OCT) camera was used to image and quantify CFP+ cells in mice from the B6.Cg-Tg(Thy1-CFP)23Jrs/J line. SD-OCT circle (1 B-scan), raster (37 B-scans) and radial (24 B-scans) scans of the retina were also obtained. CSLO was performed at baseline (n = 11) and 3 (n = 11), 5 (n = 4), 7 (n = 10), 10 (n = 6), 14 (n = 7) and 21 (n = 5) days post-transection, while SD-OCT was performed at baseline and 7, 14 and 35 days (n = 9) post-transection. Longitudinal change in CFP+ cell density and retinal thickness were computed. Compared to baseline, the mean (SD) percentage CFP+ cells remaining at 3, 5, 7, 10, 14 and 21 days post-transection was 86 (9)%, 63 (11)%, 45 (11)%, 31 (9)%, 20 (9)% and 8 (4)%, respectively. Compared to baseline, the mean (SD) retinal thickness at 7 days post-transection was 97 (3)%, 98 (2)% and 97 (4)% for the circle, raster and radial scans, respectively. The corresponding figures at 14 and 35 days post-transection were 96 (3)%, 97 (2)% and 95 (3)%; and 93 (3)%, 94 (3)% and 92 (3)%. CONCLUSIONS/SIGNIFICANCE: Longitudinal imaging showed an exponential decline in CFP+ cell density and a small (≤8%) reduction in SD-OCT measured retinal thickness post-transection. SD-OCT is a promising tool for detecting structural changes in experimental optic neuropathy. These results represent an important step towards translation for clinical use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".