Effect of Oryctes rhinoceros larva oil supplementation on serum lipid profile and inflammatory markers in mice fed a cholesterol-based diet
Bibliographic record
Abstract
A cholesterol-enriched diet has been shown to adversely affect lipoprotein profiles and increase cardiovascular disease risk.Dietary cholesterol plays an important role in modulating inflammatory responses involved in atherosclerosis.In the present study, the effect of Oryctes rhinoceros larva oil (ORO), an unsaturated fatty acid-rich animal fat, on serum lipid profile and some proinflammatory markers in mice fed a cholesterol-based diet (CBD) was investigated.Forty male Swiss albino mice were randomly assigned to four groups consisting of control (normal diet) and three experimental groups fed normal diet supplemented with ORO, CBD only and CBD supplemented with ORO, respectively.Serum lipid profile, malondialdehyde, C-reactive protein, interleukin-6 and tumour necrosis factor-alpha levels were evaluated before and after diet treatment.Serum triacyglycerol, total cholesterol and low-density lipoprotein cholesterol levels were significantly reduced (P<0.05) in mice fed a CBD diet supplemented with ORO compared with those fed CBD without ORO.In addition, serum malondialdehyde, C-reactive protein and interleukin-6 levels were significantly lower in mice fed CBD suplemented with ORO compared with those fed CBD only (P<0.05).These results suggest that consumption of ORO improved the serum lipid profile and, in addition, may mitigate the attendant adverse inflammatory processes in atherosclerosis.
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) | 0.001 | 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".