Processed Tomato Products as a Source of Dietary Lycopene:<i>Bioavailability and Antioxidant Properties</i>
Bibliographic record
Abstract
Oxidative stress is one of the major contributors to increased risk of chronic diseases. A diet rich in tomatoes and tomato products containing lycopene, a carotenoid antioxidant, has been found to protect against these chronic diseases by mitigating oxidative damage. The study aim was to evaluate the effects of a long-term tomato-rich diet, consisting of various processed tomato products, on bioavailability and antioxidant properties of lycopene. Seventeen healthy human subjects (ten men, seven non-pregnant women) participated in the study. Following a two-week washout period during which subjects avoided foods containing lycopene, all subjects consumed test tomato products including tomato juice, tomato sauce, tomato paste, ketchup, spaghetti sauce, and ready-to-serve tomato soup providing 30 mg of lycopene a day for four weeks. At the end of treatment, serum lycopene level increased significantly (p <0.05), from 181.79 +/- 31.25 to 684.7 +/- 113.91 nmol/L. Similarly, total antioxidant potential increased significantly (p <0.05), from 2.26 +/- 0.015 to 2.38 +/- 0.17 mmol/L Trolox equivalent. Lipid and protein oxidation was reduced significantly (p <0.05). The results suggest that a tomato-rich diet containing different sources of lycopene can increase serum lycopene levels and reduce oxidative stress effectively.
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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".