In Vivo Inhibition of Growth of Human Tumor Lines by Flavonoid Fractions From Cranberry Extract
Bibliographic record
Abstract
Edible fruits and berries may serve as sources for novel anticancer agents, given that extracts of these foods have demonstrated cytotoxic activity against tumor cell lines. Semipurified, flavonoid-rich extracts of cranberry (Vaccinia macrocarpa) were shown previously to arrest proliferation of tumor cells and induce apoptosis. However, the ability of cranberry flavonoids to inhibit tumor growth in vivo has not been reported other than in a preliminary report. As model systems for testing this activity, human tumor cell lines representative of three malignancies were chosen: glioblastoma multiforme (U87), colon carcinoma (HT-29), and androgen-independent prostate carcinoma (DU145). A flavonoid-rich fraction 6 (Fr6) and a more purified proanthocyanidin (PAC)-rich fraction were isolated from cranberry presscake and whole cranberry, respectively, by column chromatography. Fr6 and PAC each significantly slowed the growth of explant tumors of U87 in vivo, and PAC inhibited growth of HT-29 and DU145 explants (P < 0.05), inducing complete regression of two DU145 tumor explants. Flow cytometric analyses of in vitro-treated U87 cells indicated that Fr6 and PAC could arrest cells in G1 phase of the cell cycle (P < 0.05) and also induce cell death within 24 to 48 h of exposure (P < 0.05). These results indicate the presence of a potential anticancer constituent in the flavonoid-containing fractions from cranberry extracts.
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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) | 0.001 | 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 | 0.001 |
| 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".