Supranormal Electroretinogram in<i>F</i><i>at-1</i>Mice with Retinas Enriched in Docosahexaenoic Acid and n<i>-</i>3 Very Long Chain Fatty Acids (C24–C36)
Bibliographic record
Abstract
PURPOSE: Fat-1 mice can convert n-6 to n-3 fatty acids endogenously, resulting in the accumulation of n-3 fatty acids in major tissues. This was a study of how this conversion affects the major fatty acid found in retina, n-3 docosahexaenoic acid (DHA), the very long chain fatty acids (VLCFA, C24-C36), and retinal function. METHODS: Both wild-type (WT) and fat-1 mice were fed a modified AIN-93G diet containing 10% safflower oil, high in 18:2n-6. Fatty acid composition of individual phospholipids was analyzed in total lipid extracts from whole eyes excluding the lens. Retinal function and levels of proteins involved in cellular stress were assessed with full field electroretinogram (ERG) recordings and immunohistochemistry, respectively. RESULTS: Compared with WT mice, DHA levels in fat-1 mice increased two to five times in all phospholipid classes, whereas n-6 fatty acid levels decreased. Levels of C32 and C34 n-3 pentaenoic and hexaenoic VLCFA in phosphatidylcholine increased whereas n-6 VLCFAs were depleted. Scotopic and photopic ERGs showed unusually high amplitudes for both a- and b-waves and lower thresholds in fat-1 mice. Glial fibrillary acidic protein (GFAP) and carboxyethylpyrrole (CEP, protein adducts produced from DHA oxidation) were respectively increased in Müller cells and photoreceptors of fat-1 mice. CONCLUSIONS: Highly enriched DHA and n-3 VLCFA in the retina lead to supernormal scotopic and photopic ERGs and increases in Müller cell reactivity and oxidative stress in photoreceptors. The regulation of n-3 fatty acids levels and of the n-6/n-3 fatty acid ratio are essential in preserving retinal integrity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".