Dietary LA and sex effects on oxylipin profiles in rat kidney, liver, and serum differ from their effects on PUFAs
Bibliographic record
Abstract
A vast literature on fatty acids in mammals exists, but comparable compositional data on oxylipins is lacking. Weanling Sprague-Dawley rats were therefore provided control diets or diets with higher linoleic acid (LA) or with higher LA and α-linolenic acid (LA+ALA) for 6 weeks. Kidneys, livers, and serum were analyzed for oxylipins and fatty acids. The proportion of tissue oxylipins derived from LA was greater than the relative proportion of LA itself, whereas arachidonic acid (AA) oxylipins were overrepresented in serum. Higher dietary LA increased kidney LA and AA oxylipins, despite not altering LA or AA. In liver, both LA and AA and their oxylipins were higher, whereas in serum only LA oxylipins were higher with higher dietary LA. Higher LA resulted in a higher ratio of n-6/n-3 PUFA-derived oxylipins; adding ALA to the LA diet mitigated this and many, but not all, effects of the LA diet. Approximately 40% of oxylipins detected were influenced by sex and, unlike their PUFA precursors, most (>90%) of these were higher in males. These differences in dietary LA and sex on oxylipin and fatty acid profiles further our understanding of the effects of fatty acids and may have implications for dietary LA recommendations.
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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) | 0.000 | 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) | 0.003 | 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".