Combined dietary omega-3 and omega-6 fatty acids protect against hyperglycemia-associated retinopathy in neonatal mice
Bibliographic record
Abstract
Retinopathy of prematurity (ROP) with early vessel loss (Phase I) followed by uncontrolled vessel growth (Phase II) causes visual impairment in premature infants. Although supplementation with omega-3 (n-3) docosahexaenoic acid (DHA) alone shows mixed results in preventing ROP, supplementation with both n-3 DHA and n-6 arachidonic acid (ARA) in early postnatal life reduces severe ROP by 50 % (Mega Donna Mega study). In the Mega Donna Mega study, 146 (72.6 %) of 201 included infants had at least one hyperglycemic episode during the first 14 days of life, which is a strong ROP risk factor. We therefore evaluated the protective effects and mechanisms of combined dietary n-3 DHA and n-6 ARA in a neonatal mouse model of hyperglycemia-induced suppression of retinal vascular development (Phase I ROP). At postnatal day (P) 10, retinal vessel growth was improved in pups from mothers on diets enriched with 1 % DHA + 2 % ARA vs. 3 % DHA. Lipid changes in pup plasma and RPE complex (retinal pigment epithelium with choroid and sclera) were in accordance with maternal diets' DHA and ARA levels, indicating that milk lipids reflected maternal diets. Proteomic retinal analysis revealed increased abundances of proteins related to mitochondrial respiration and glucose metabolism with the combined diet. Inhibition of mitochondrial ATP synthase negated the protective effects of the combined diet. In conclusion, combined DHA+ARA oral maternal supplementation protects against hyperglycemia-induced retinopathy in mouse neonates (Phase I ROP model) through enhanced retinal metabolism, suggesting the potential of balanced lipid supplementation for ROP prevention.
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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 | 0.001 | 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 | 0.000 |
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".