Correlation between SD‐OCT, immunocytochemistry and functional findings in an animal model of retinal degeneration
Bibliographic record
Abstract
Abstract Purpose The P23H rhodopsin mutation is an extensively studied model of ADRP. We evaluated the anatomical changes using SD‐OCT and correlate the findings and retinal thickness values with immunocytochemistry. Functional changes were analyzed Methods Heterozygous P23H pigmented transgenic rats aged from P18 to P180 were studied. LE rats bred with Sprague Dawley (SD) 1 month old served as wild type controls. Visual acuity and contrast sensitivity evaluation was performed every month. Corneal ERGs were recorded under scotopic and photopic conditions. Retinal thicknesses at different levels (total thickness, ONL + RPE, ONL and IPL), fundus autofluorescence (FAF) and fluorescein angiography was performed in 3 animals at P150 using Spectralis OCT and HRA (Heidelberg Engineering, Germany). Retinas were immunostained for ICC. Results Retinal thicknesses diminution was seen in OCT sections, with a clear loss of ONL and morphological modifications. Statically differences were found between groups in all evaluated thicknesses. In the P23H rats, change in FAF was noted comparing to control group, as sparse autofluorescent dots. No relevant changes were observed in the angiography pattern. ICC showed a progressive decrease in ONL thickness. Functional changes were progressive with time. Conclusion Anatomical changes in pigmented P23H can be observed using SD‐OCT and immunocytochemistry, with a good correlation between their values. SD‐OCT and FAF are important tools for research in retinal degenerations.
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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".