Vaccenic acid suppresses intestinal inflammation by increasing anandamide and related N-acylethanolamines in the JCR:LA-cp rat
Bibliographic record
Abstract
Vaccenic acid (VA), the predominant ruminant-derivedtransfat in the food chain, ameliorates hyperlipidemia, yet mechanisms remain elusive. We investigated whether VA could influence tissue endocannabinoids (ECs) by altering the availability of their biosynthetic precursor, arachidonic acid (AA), in membrane phospholipids (PLs). JCR:LA-cprats were assigned to a control diet with or without VA (1% w/w),cis-9,trans-11 conjugated linoleic acid (CLA) (1% w/w) or VA+CLA (1% + 0.5% w/w) for 8 weeks. VA reduced the EC, 2-arachidonoylglycerol (2-AG), in the liver and visceral adipose tissue (VAT) relative to control diet (P< 0.001), but did not change AA in tissue PLs. There was no additive effect of combining VA+CLA on 2-AG relative to VA alone (P> 0.05). Interestingly, VA increased jejunal concentrations of anandamide and those of the noncannabinoid signaling molecules, oleoylethanolamide and palmitoylethanolamide, relative to control diet (P< 0.05). This was consistent with a lower jejunal protein abundance (but not activity) of their degrading enzyme, fatty acid amide hydrolase, as well as the mRNA expression of TNFα and interleukin 1β (P< 0.05). The ability of VA to reduce 2-AG in the liver and VAT provides a potential mechanistic explanation to alleviate ectopic lipid accumulation. The opposing regulation of ECs and other noncannabinoid lipid signaling molecules by VA suggests an activation of benefit via the EC system in the intestine.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".