Supplementation With Highly Purified Docosahexaenoic Acid Ethyl Ester Was Associated With Enhancement of Hepatic Fatty Acid Oxidation and Greater Apparent Absorption of Docosahexaenoic Acid in Rats
Bibliographic record
Abstract
Fish oil-derived fatty acid ethyl ester (EE) has been widely utilized as a supplement in Europe and United States. However, few studies have assessed the apparent absorption of docosahexaenoic acid (DHA) between non-purified DHA-EE (L-DHAEE) and highly purified DHA-EE (H-DHAEE). This study evaluated the effects of L-DHAEE and H-DHAEE on lipid metabolism and apparent absorption of DHA in rats. Groups of male Wistar rats were fed AIN93G diet containing 2.8% L-DHAEE or 1.0% H-DHAEE for 4 or 8 weeks. As parameters of lipid metabolism and apparent absorption of DHA-EE, serum and liver lipid contents, fecal EE excretion, lipid-metabolizing enzyme activities, and organ fatty acid composition were measured. The L-DHAEE diet increased fecal DHA-EE and total EE excretion compared with the H-DHAEE diet. In rats fed DHA-EE for 4 weeks, H-DHAEE was more efficacious than L-DHAEE at increasing liver DHA content, partly owing to the enhancement of apparent absorption. In addition, rats fed the H-DHAEE diet had higher fatty acid ?-oxidation activity in peroxisomes than rats fed the L-DHAEE diet for 8 weeks. These results suggest that H-DHAEE supplementation may have beneficial functions in the prevention of lifestyle-related diseases as compared with L-DHAEE supplementation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".