Marine Sources but Not Plant Sources of Omega‐3 Fatty Acids Prevents Progression of Hepatic Steatosis in <i>fa/fa</i> Zucker Rats
Bibliographic record
Abstract
Objective To compare omega‐3 polyunsaturated fatty acid (n3 PUFA) diets rich in α‐linoleic acid (ALA), eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA) on hepatic steatosis in fa/fa Zucker rats. Methods Six week old fa/fa rats were fed n3 PUFA diets containing ALA (faALA), EPA (faEPA) or DHA (faDHA) for 8 weeks relative to a linoleic acid (LA)‐rich n6 PUFA diet fed to fa/fa (faLA) and lean (lnLA) rats. Comparisons were to baseline fa/fa rats (faBASE). Body weight and feed intake were monitored. Fasting serum was obtained at 0 and 8 weeks, and liver samples were collected at week 8. Results faEPA and faDHA had less body weight gain compared to faLA, but faALA was not different from faLA. faDHA had total liver lipids levels similar to faBASE, while faALA had the most hepatic steatosis. This was unrelated to liver weights, which did not differ among the fa/fa groups. There was no differences in liver lipid vesicle size among the fa/fa rats, but there was a trend in the distribution of vesicle sizes with faLA having the largest followed by faALA, faEPA and faDHA. faDHA and faLA had lower fasting insulin compared to faALA and faEPA, while the baseline groups and lnLA had the lowest fasting insulin levels overall. AUCinsulin was lower in faDHA compared to faALA but similar to faEPA and faLA.HOMA‐IR was highest in faALA and faEPA, but was not different from faDHA. faEPA had lower fasting serum triglycerides than faBASE and faALA. Conclusion DHA prevented further progression of hepatic steatosis in fa/fa Zucker rats and was associated with improvements in insulin resistance. ALA had the highest degree of hepatic steatosis and displayed a higher degree of insulin resistance than EPA and DHA. Supported by ARDI
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".