Alterations in Lipid-Lipoprotein Fractions and Antioxidant Status by Lycopene and its Blends with Rice Bran Oil in Nutritionally Induced Hyperlipidemic Animal Model
Bibliographic record
Abstract
Nutrition Transition in the latter half of 20th century has prompted profound shift from traditional dietary pattern of fibre rich foods to increased consumption of energy dense, high fat and low fibre foods. Resultant oblivious nutritional environment coupled with physical inactivity has abridged the gap between health and chronic diseases. Consequently, the paradigm of treatment from pharmaceuticals has shifted to neutraceuticals and a large proportion of populace is resorting to cost effective treatments from natural sources that can contribute effectually in combating these dreadful diseases. The present study was undertaken to evaluate the efficacy of blends of lycopene and rice bran oil (RBO) on alteration in lipid metabolism and oxidative stress biomarkers in high fat high cholesterol diet fed albino rats. The rats were supplemented with lycopene (30mg/kg/day) singly and blend of lycopene (30mg/kg/day) and rice bran oil (100ml/kg/day) for 6 weeks. Results indicate that lycopene enriched diets significantly (p≤0.05) improved altered parameters, however, the effect was more pronounced in animals reared on blends of lycopene and RBO. The therapeutic potential of lycopene and RBO can be tapped as preventive and protective therapy against the detrimental effects of high fat diets consumed globally.
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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 | 0.001 | 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".