<i>Morinda citrifolia</i>(Noni) Juice Augments Mammary Gland Differentiation and Reduces Mammary Tumor Growth in Mice Expressing the Unactivated c-<i>erb</i>B2 Transgene
Bibliographic record
Abstract
Morinda citrifolia (noni) is reported to have many beneficial properties, including on immune, inflammatory, quality of life, and cancer endpoints, but little is known about its ability to prevent or treat breast cancer. To test its anticancer potential, the effects of Tahitian Noni Juice (TNJ) on mammary carcinogenesis were examined in MMTV-neu transgenic mice. Mammary tumor latency, incidence, multiplicity, and metastatic incidence were unaffected by TNJ treatment, which suggests that it would not increase or decrease breast cancer risk in women taking TNJ for its other benefits. However, noni may be useful to enhance treatment responses in women with existing HER2/neu breast cancer since TNJ resulted in significant reductions in tumor weight and volume and in longer tumor doubling times in mice. Remarkably, its ability to inhibit the growth of this aggressive form of cancer occurred with the mouse equivalent of a recommended dose for humans (<3 oz/day). A 30-day treatment with TNJ also induced significant changes in mammary secondary ductule branching and lobuloalveolar development, serum progesterone levels, and estrous cycling. Additional studies investigating TNJ-induced tumor growth suppression and modified reproductive responses are needed to characterize its potential as a CAM therapy for women with and without HER2(+) breast cancer.
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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".