Therapeutic effect of garlic containing S-allyl cysteine and Diallyl-di-sulfide on improving blood lipid profile
Bibliographic record
Abstract
Cardiovascular-related diseases are considered as the major risk for health and may lead to many illnesses. Hyperlipidemia is the highest common cause of diseases related to heart known as CHD. Since ancient times, innumerable herbs and organic foods are used to treat diseases among which garlic has been shown beneficial for treating hyperlipidemia and normalizing lipid profile levels. Garlic may be used in different forms like powder, tablets, extracts or after ageing. Its sulfur containing compounds like allicin, S-allylcysteine, alliin, ajoene, diallyl disulfide are proven to have the pharmacological benefits. The main aim of the current review is to analyze and evaluate the studies regarding anti-hyperlipidemic effects of garlic and its supplements on normalizing lipid profile among hyperlipidemicsubjects.After analyzing different studies being carried out earlier, it is concluded that garlic is effective in managing lipid profile including total cholesterol, triglycerides, LDL, HDL levels among people with hyperlipidemia ranging from mild to moderate intensity.
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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 | 0.000 |
| 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".