Bibliographic record
Abstract
Morinda citrifolia (noni) fruit juice has been found to provide a wide range of potential health benefits. Among these are the reduction of free radicals and protection against lipid peroxide DNA damage in heavy cigarette smokers and athletes who had exercised to the point of exhaustion. These benefits have been observed after drinking a beverage containing a blend of noni juice from French Polynesia, Tahitian Noni® Juice (TNJ). To determine if TNJ exerts more immediate antioxidant effects, and under less extreme conditions, in vitro tests and a trial involving healthy young adult men were conducted. In the human study, volunteers drank 200 mL TNJ, orange juice or water following an overnight fast. Blood samples were collected before and 1 hour following ingestion of the beverages. Plasma and red cell lysate (erythrocyte) samples were measured for antioxidant activity potentiometrically. TNJ exhibited high in vitro antioxidant activity in the 2,2-diphenylpicrylhydrazyl (DPPH) radical scavenging, reducing power and lipid hydroperoxide scavenging assays. TNJ also significantly increased mean antioxidant activity in plasma and erythrocytes of healthy volunteers. The effect of TNJ in erythrocytes was approximately 4.6 times greater than that of orange juice. There were no increases observed in the water group. The results of this study reveal that TNJ can provide antioxidant benefits shortly after ingestion and under more everyday conditions, not only in extreme circumstances or when consumed repeatedly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".