Oils rich in α-linolenic acid independently protect against characteristics of fatty liver disease in the Δ6-desaturase null mouse
Bibliographic record
Abstract
Alpha-linolenic acid's (ALA) biological activity is poorly understood and primarily associated with its conversion to eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Delta-6 desaturase (D6D) initiates the metabolism of linoleic acid (LA) and ALA to arachidonic acid, EPA, and DHA, respectively. In this study, D6D knock-out (D6KO) mice were used to evaluate the effects of ALA-rich oils in preventing hepatic steatosis and inflammation. D6KO and wild-type mice were fed 1 of 4 high-fat (14% w/w) diets: (i) lard (LD, 0% n-3 PUFA), (ii) canola oil + ARASCO (CD, 8% ALA), (iii) flax seed oil + ARASCO (FD, 55% ALA), (iv) menhaden oil (MD, 30% EPA/DHA) for 8 or 20 weeks. Livers of D6KO mice consuming CD and FD were depleted of EPA/DHA, and enriched in ALA. Markers of fat accumulation and inflammation were lowest in the MD-fed mice, at 8 and 20 weeks, regardless of genotype. CD- and FD-fed D6KO groups were found to have lower liver lipid accumulation and lower hepatic inflammation relative to the LD-fed mice at 8 weeks. In conclusion, while MD was the most protective, this study shows that ALA can act independently on risk factors associated with the development of fatty liver disease.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".