Correlation between SD-OCT, immunocytochemistry and functional findings in an animal model of retinal degeneration
Bibliographic record
Abstract
PURPOSE: The P23H rhodopsin mutation is an autosomal dominant cause of retinitis pigmentosa (RP). The degeneration can be tracked using different anatomical and functional methods. In our case, we evaluated the anatomical changes using Spectral-Domain Optical Coherence Tomography (SD-OCT) and correlated the findings with retinal thickness values determined by immunocytochemistry. METHODS: Pigmented rats heterozygous for the P23H mutation, with ages between P18 and P180 were studied. Function was assessed by means of optomotor testing and ERGs. Retinal thicknesses measurements, autofluorescence and fluorescein angiography were performed using Spectralis OCT. Retinas were studied by means of immunohistochemistry. RESULTS: Between P30 and P180, visual acuity decreased from 0.500 to 0.182 cycles per degree (cyc/deg) and contrast sensitivity decreased from 54.56 to 2.98 for a spatial frequency of 0.089 cyc/deg. Only cone-driven b-wave responses reached developmental maturity. Flicker fusions were also comparable at P29 (42 Hz). Double flash-isolated rod-driven responses were already affected at P29. Photopic responses revealed deterioration after P29.A reduction in retinal thicknesses and morphological modifications were seen in OCT sections. Statistically significant differences were found in all evaluated thicknesses. Autofluorescence was seen in P23H rats as sparse dots. Immunocytochemistry showed a progressive decrease in the outer nuclear layer (ONL), and morphological changes. Although anatomical thickness measures were significantly lower than OCT values, there was a very strong correlation between the values measured by both techniques. CONCLUSIONS: In pigmented P23H rats, a progressive deterioration occurs in both retinal function and anatomy. Anatomical changes can be effectively evaluated using SD-OCT and immunocytochemistry, with a good correlation between their values, thus making SD-OCT an important tool for research in retinal degeneration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".