Apple Peel Flavonoid Fraction 4 Suppresses Breast Cancer Cell Growth by Cytostatic and Cytotoxic Mechanisms
Bibliographic record
Abstract
Many dietary flavonoids possess anti-cancer activities. Here, the effect of apple peel flavonoid fraction 4 (AF4) on the growth of triple-negative (MDA-MB-231, MDA-MB-468), estrogen receptor-positive (MCF-7), and HER2-positive (SKBR3) breast cancer cells was determined and compared with the effect of AF4 on normal mammary epithelial cells and dermal fibroblasts. AF4 inhibited breast cancer cell growth in monolayer cultures, as well as the growth of MCF-7 spheroids, without substantially affecting the viability of non-malignant cells. A sub-cytotoxic concentration of AF4 suppressed the proliferation of MDA-MB-231 cells by inhibiting passage through the G0/G1 phase of the cell cycle. AF4-treated MDA-MB-231 cells also exhibited reduced in vitro migration and invasion, and decreased Akt (protein kinase B) signaling. Higher concentrations of AF4 were selectively cytotoxic for MDA-MB-231 cells. AF4 cytotoxicity was associated with the intracellular accumulation of reactive oxygen species. Importantly, intratumoral administration of AF4 suppressed the growth of MDA-MB-231 xenografts in non-obese diabetic severe combined immunodeficient (NOD-SCID) female mice. The selective cytotoxicity of AF4 for breast cancer cells, combined with the capacity of sub-cytotoxic AF4 to inhibit breast cancer cell proliferation, migration, and invasion suggests that flavonoid-rich AF4 (and its constituents) has potential as a natural therapeutic agent for breast cancer treatment.
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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".