The effect of linoleic acid on the whole body synthesis rates of polyunsaturated fatty acids from α-linolenic acid and linoleic acid in free-living rats
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is thought to be important for brain function. The main dietary source of DHA is fish, however, DHA can also be synthesized from precursor omega-3 polyunsaturated fatty acids (n-3 PUFA), the most abundantly consumed being α-linolenic acid (ALA). The enzymes required to synthesize DHA from ALA are also used to synthesize longer chain omega-6 (n-6) PUFA from linoleic acid (LNA). The large increase in LNA consumption that has occurred over the last century has led to concern that LNA and other n-6 PUFA outcompete n-3 PUFA for enzymes involved in DHA synthesis, and therefore, decrease overall DHA synthesis. To assess this, rats were fed diets containing LNA at 53 (high LNA diet), 11 (medium LNA diet) or 1.5% (low LNA diet) of the fatty acids with ALA being constant across all diets (approximately 4% of the fatty acids). Rats were maintained on these diets from weaning for 8 weeks, at which point they were subjected to a steady-state infusion of labeled ALA and LNA to measure DHA and arachidonic acid (ARA) synthesis rates. DHA and ARA synthesis rates were generally highest in rats fed the medium and high LNA diets, while the plasma half-life of DHA was longer in rats fed the low LNA diet. Therefore, increasing dietary LNA, in rats, did not impair DHA synthesis; however, low dietary LNA led to a decrease in DHA synthesis with tissue concentrations of DHA possibly being maintained by a longer DHA half-life.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".