Fish oil induced hyperlipidemia and oxidative stress in BioF1B hamsters is attenuated by elderberry extract
Bibliographic record
Abstract
We have previously reported fish oil induced hyperlipidemia in BioF1B hamsters compared with Golden Syrian (GS) hamsters. Elderberry (Sambucus nigra L.) extract is abundant in anthocyanins and is believed to exert cardioprotective effects primarily by virtue of its hypolipidemic and antioxidant potential. In the current study, high-fat fish oil feeding increased oxidative stress in BioF1B hamsters compared with GS hamsters; this increase was associated with increased levels of omega-3 polyunsaturated fatty acids in plasma and liver. We then investigated whether cosupplementation with anthocyanin-rich elderberry extract would reverse fish oil induced hyperlipidemia and reduce lipid peroxidation in BioF1B hamsters. Plasma and hepatic lipids decreased significantly when hamsters were fed diets containing elderberry extract along with fish oil. Both plasma and liver thiobarbituric acid reactive substances showed significant reductions upon cosupplementation with elderberry extract in fish oil fed BioF1B hamsters. Our findings demonstrate that cosupplementation with elderberry extract reverses hyperlipidemia and lipid peroxidation observed with dietary fish oil alone in BioF1B hamsters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".