A high-fat plus high-sucrose diet induces age-related macular degeneration in an experimental rabbit model
Bibliographic record
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness. Metabolic disorders and diets are risk factors. We compared lipid profiles and retinal phenotypes with long-term feeding of four diets in male Chinchilla rabbits. Animals were fed a normal diet (ND), high-fat diet (HFD), high-sucrose diet (HSD) or a high-fat plus high-sucrose diet (HFSD) for 6 months. Eyes were examined using multimodal imaging modalities and electroretinograms. Retinal sections were analyzed using H&E staining, Toluidine Blue staining, immunostaining and transmission electron microscopy. Lipids and complement C3 protein (C3) in serum or aqueous humor were measured. RNA sequencing was performed to evaluate the retinal transcriptomes. HFD and HSD had minor effects on lipid profiles but, when fed concomitantly, synergistically induced severe dyslipidemia. None of the four diets caused obesity. HFSD induced retinal lesions, such as reticular pseudodrusen (RPDs) and other pigmentary abnormalities. RPD-like lesions were mainly lipid droplets around cells of the retinal pigment epithelium. HFSD also induced elevated levels of ocular C3 and reduced the density of retinal vessels. In conclusion, HFD and HSD can - when combined - induce normal-weight dyslipidemia and RPD-like retinal lesions. HFSD-fed male Chinchilla rabbits are a good model of early AMD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".