Linoleic acid, <i>α</i>-linolenic acid and enterolactone affect lipid oxidation and expression of lipid metabolism and antioxidant-related genes in hepatic tissue of dairy cows
Bibliographic record
Abstract
Although beneficial effects have been attributed to PUFA supplementation in high-yielding dairy cows, diets rich in PUFA may also increase oxidative stress in tissues such as the liver. To fully exploit the health benefits of PUFA, we believe that the addition of natural antioxidants could help in preventing oxidative damage. Using an in vitro precision-cut liver slices (PCLS) tissue culture system, we investigated the effects of different linoleic acid (LA, n-6):α-linolenic acid (ALA, n-3) ratios (LA:ALA ratio of 4, LA:ALA ratio of 15 and LA:ALA ratio of 25) in the presence or absence of the antioxidant enterolactone (ENL) on (1) the mRNA abundance of genes with key roles in hepatic lipid metabolism, oxidative stress response and inflammatory processes, (2) oxidative damages to lipids and proteins and (3) superoxide dismutase activity in early-lactating dairy cows. The addition of LA and ALA to PCLS culture media increased oxidative damage to lipids as suggested by higher concentrations of thiobarbituric acid reactive substances and increased the expression of nuclear factor erythroid 2-related factor 2 target genes. The addition of ENL was effective in preventing lipid peroxidation caused by LA and ALA. Transcript abundance of sterol regulatory element-binding transcription factor 1 and its lipogenic target genes acetyl-CoA carboxylase α, fatty acid synthase (FASN) and stearoyl-CoA desaturase (SCD) was decreased with LA and ALA, whereas ENL decreased FASN and SCD gene expression. Our results show that addition of LA and ALA to PCLS culture media lowers hepatic lipogenic gene expression and increases oxidative damages to lipids. On the other hand, addition of ENL prevents oxidative damages provoked by these PUFA.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".