Oils rich in alpha‐linolenic acid independently protect against characteristics of fatty liver disease in the delta‐6‐desaturase null mouse
Bibliographic record
Abstract
Non‐alcoholic fatty liver disease (NAFLD) is a growing health concern. The study objective was to use the novel Δ‐6‐desaturase null (D6KO) mouse to determine if α‐linolenic acid (ALA) can independently prevent hepatic steatosis and inflammation. Experimental groups include male wild type (WT) or D6KO mice fed a high fat diet (30% fat) containing either: lard (LD), canola oil (CD,11% ALA), flax oil (FD,50% ALA), or fish oil (MD, n‐3 HUFA) (n=4–7/group) for 8 or 20 weeks. At 8 weeks mean hepatic inflammation scores for D6KO CD and FD groups were intermediate between LD and MD scores, and lower than WT scores. In contrast, D6KO FD and CD groups had higher hepatic steatosis scores relative to WT mice. FD and MD D6KO groups had lower liver lipid mass relative to LD‐fed D6KO mice. Similar trends were seen at 20 weeks. Gas chromatography confirmed the absence of n‐3 HUFA (LD, CD, FD) and of enrichment ALA (CD, FD) in D6KO liver tissue. Results confirm that perturbations in essential fatty acid metabolism lead to the development of fatty liver, and suggest that oils rich in ALA can independently prevent hepatic inflammation. Supported by Canola Council of Canada, NSERC, CFI/ORF to D.Ma and OGS to J. Monteiro Grant Funding Source : Canola Council of Canada, NSERC
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".