Differential effect of a carotenoid-rich diet on retina function in non-diabetic and diabetic rats
Bibliographic record
Abstract
Objective: This study was designed to examine the supplementation of a carotenoid-rich carrot powder, on retina function and carotenoid metabolism in non-diabetic control and type 1 diabetic animals.Methods: Male Wistar rats (n = 30) were randomly assigned to diets supplemented with (n = 15) or without (n = 15) carrot powder enriched diets (150 g/kg diet). After 3 weeks of diet adaptation, 8 rats in each group were treated with streptozotocin (iv) to induce type 1 diabetes and fed for a further 9 wk. Retinal function was assessed with the electroretinogram (ERG). Hepatic and plasma retinoids and carotenoids were measured by ultra-performance liquid chromatography.Results: Non-diabetic control rats fed the carrot diet had significantly (p < 0.02) higher rod- and cone- driven post-synaptic b-wave amplitudes, respectively, compared to those fed the control diet. These functional changes correlated with higher (p < 0.05) liver levels of carotenoids (α- and β- carotene) and retinoids. In diabetic rats, carrot diet exacerbated retina dysfunction; the amplitudes for most of rod- and cone-driven ERG components were the lowest amplitudes among all groups (p < 0.02). Diabetic rats fed the carrot diet had lower hepatic retinol and retinyl palmitate, while having higher α- and β-carotene levels, indicating diminished hepatic conversion of carotenoids into retinoids.Discussion: Dietary supplementation of high dose dietary carotenoids plays a beneficial role on healthy rat retina function, but exerts a detrimental effect in diabetes, which warrants undertaking detailed mechanistic studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".