Olive oil supplemented with Coenzyme Q<sub>10</sub>: Effect on plasma and lipoprotein oxidative status
Bibliographic record
Abstract
Olive oil consumption is associated with protective cardiovascular properties, including some beneficial modifications in lipoprotein profile and composition. Coenzyme Q(10) (CoQ(10)) exerts a protective effect on plasma lipoproteins. Aim of the study was to investigate whether extra virgin (EV) olive oil enriched with CoQ(10) affects CoQ(10) levels and oxidative status in plasma and in isolated lipoproteins. Twelve subjects were administered 20 mL olive oil per day for 2 weeks, followed by 2 weeks of olive oil enriched with 20 mg and 2 more weeks with 40 mg of CoQ(10). Plasma and isolated lipoproteins were collected in each phase of the study and subsequently analyzed to assess lipid profile, CoQ10 levels, ORAC assay, resistance of lipoproteins to peroxidation and paroxonase 1 activity. Plasma CoQ(10) levels significantly increased with the 20 mg (+73%) and 40 mg dose (+170%), while the percentage of oxidized CoQ(10) decreased. A significant inverse correlation was found in plasma between percentage of oxidized CoQ(10) and total antioxidant capacity. A lower susceptibility of LDL to peroxidation was also found. Finally, a positive correlation was observed between concentration of CoQ(10) in HDL and paraoxonase-1 activity. EV olive oil enriched with both doses of CoQ(10) significantly affects its bioavailability and plasma redox status. These changes are associated with a decreased susceptibility of plasma lipoproteins to peroxidation associated with a chain-breaking antioxidant activity of the formulation.
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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 | 0.000 | 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) | 0.001 | 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".