Effects of Maple (<i>Acer</i>) Plant Part Extracts on Proliferation, Apoptosis and Cell Cycle Arrest of Human Tumorigenic and Non‐tumorigenic Colon Cells
Bibliographic record
Abstract
Phenolic-enriched extracts of maple sap and syrup, obtained from the sugar and red maple species (Acer saccharum Marsh, A. rubrum L., respectively), are reported to show anticancer effects. Despite traditional medicinal uses of various other parts of these plants by Native Americans, they have not been investigated for anticancer activity. Here leaves, stems/twigs, barks and sapwoods of both maple species were evaluated for antiproliferative effects against human colon tumorigenic (HCT-116, HT-29, Caco-2) and non-tumorigenic (CCD-18Co) cells. Extracts were standardized to total phenolic and ginnalin-A (isolated in our laboratory) levels. Overall, the extracts inhibited the growth of the colon cancer more than normal cells (over two-fold), their activities increased with their ginnalin-A levels, with red > sugar maple extracts. The red maple leaf extract, which contained the highest ginnalin-A content, was the most active extract (IC₅₀ = 35 and 16 µg/mL for extract and ginnalin-A, respectively). The extracts were not cytotoxic nor did they induce apoptosis of the colon cancer cells. However, cell cycle analyses revealed that the antiproliferative effects of the extracts were mediated through cell cycle arrest in the S-phase. The results from the current study suggest that these maple plant part extracts may have potential anticolon cancer effects.
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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".