Noninvasive, In Vivo Assessment of Mouse Retinal Structure Using Optical Coherence Tomography
Bibliographic record
Abstract
BACKGROUND: Optical coherence tomography (OCT) is a novel method of retinal in vivo imaging. In this study, we assessed the potential of OCT to yield histology-analogue sections in mouse models of retinal degeneration. METHODOLOGY/PRINCIPAL FINDINGS: We achieved to adapt a commercial 3(rd) generation OCT system to obtain and quantify high-resolution morphological sections of the mouse retina which so far required in vitro histology. OCT and histology were compared in models with developmental defects, light damage, and inherited retinal degenerations. In conditional knockout mice deficient in retinal retinoblastoma protein Rb, the gradient of Cre expression from center to periphery, leading to a gradual reduction of retinal thickness, was clearly visible and well topographically quantifiable. In Nrl knockout mice, the layer involvement in the formation of rosette-like structures was similarly clear as in histology. OCT examination of focal light damage, well demarcated by the autofluorescence pattern, revealed a practically complete loss of photoreceptors with preservation of inner retinal layers, but also more subtle changes like edema formation. In Crb1 knockout mice (a model for Leber's congenital amaurosis), retinal vessels slipping through the outer nuclear layer towards the retinal pigment epithelium (RPE) due to the lack of adhesion in the subapical region of the photoreceptor inner segments could be well identified. CONCLUSIONS/SIGNIFICANCE: We found that with the OCT we were able to detect and analyze a wide range of mouse retinal pathology, and the results compared well to histological sections. In addition, the technique allows to follow individual animals over time, thereby reducing the numbers of study animals needed, and to assess dynamic processes like edema formation. The results clearly indicate that OCT has the potential to revolutionize the future design of respective short- and long-term studies, as well as the preclinical assessment of therapeutic strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".