Influence of Functional Sweet White Lupin Biscuits on Lipid Profile and Food Efficiency of Induced Hyperlipidemia Rats
Bibliographic record
Abstract
In recent years, functional foods have attracted much interest to prevent nutrition-related diseases such as hyperlipidemia and weight gain. In this regard, this study was designed to examine the effect of use sweet white lupin (SWL) oil and flour with/without germination as a source of active healthy components to prepare functional biscuits for lowering blood lipids and growth. Functional biscuits were formulated by replacing wheat flour and butter in biscuit formulae by SWL extracted flour and SWL oil in the range of 20-30% (w/w) and 30-40% (v/w), respectively. Results indicated that the feed of hyperlipidemic rats on diets supplemented with different functional SWL biscuits for 6 weeks significantly (P < 0.05) reduced serum total cholesterol, triglycerides, low density lipoproteins, very low density lipoproteins, ratio of total cholesterol/high density lipoproteins cholesterol, ratio of low density lipoproteins/high density lipoproteins cholesterol and atherogenic index. Furthermore, the feed of functional SWL biscuits significantly reduced the body weight gain of rats and their food efficiency compared to that of rats fed on hyperlipidemic diet. On the other hand, there was an increase in the value of high density lipoproteins cholesterol and its ratio with total cholesterol. All these findings supported that the addition of 25% germinated SWL flour and 35% or 40% germinated SWL oil in biscuits gave interested results compared to the common wheat biscuits. Therefore, the proposed functional SWL biscuits could be able to regulate the blood cholesterol and the body growth levels of individuals and patients.
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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 | 0.000 |
| 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".