Artificial rearing with docosahexaenoic acid and n-6 docosapentaenoic acid alters rat tissue fatty acid composition
Bibliographic record
Abstract
Docosahexaenoic acid (DHA; 22:6n-3) and n-6 docosapentaenoic acid (DPAn-6; 22:5n-6) are components of enriched animal feed and oil derived from Schizochytrium species microalgae. A one generation, artificial rearing model from day 2 after birth onward (AR) and a dam-reared control group (DAM) were used to examine DPAn-6 feeding on the fatty acid composition of various rat tissues at 15 weeks of age. Four AR diets were based on an n-3 fatty acid-deficient, 18:2n-6-based artificial milk with 22:6n-3 and/or 22:5n-6 added: AR-LA, AR-DHA, AR-DPAn-6, and AR-DHA+DPAn-6. The 22:6n-3 levels for the DAM, AR-DHA, and AR-DHA+DPAn-6 groups tended to be similar and higher than in the AR-LA and AR-DPAn-6 groups. The levels of 22:5n-6 tended to be higher only in the absence of dietary 22:6n-3. Adipose levels of 22:5n-6 was the only exception, as 22:5n-6 was significantly higher in AR-DHA+DPAn-6 than was observed in either the DAM or the AR-DHA group. There were no differences in 20:4n-6 levels within the tissues examined. In conclusion, 22:5n-6 replaces 22:6n-3 in the absence of 22:6n-3 only and does not appear to compete with 22:6n-3 in the presence of dietary 22:6n-3, suggesting that oils containing 22:5n-6 and 22:6n-3 may be a good dietary source of 22:6n-3.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".