Anticancer natural products from traditionally used Canadian medicinal plants
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed malignant neoplasm among the female population worldwide [1] and, despite significant advances in screening technologies and therapies, it remains the second leading cause of cancer related deaths in Canadian women [2]. It is therefore imperative that we continue to develop novel and more specific anticancer agents of greater efficacy and diminished toxicity. Combining natural products research with ethnopharmacology is an effective strategy for identifying potential anticancer drug candidates [3]. The ethnobotanical knowledge of the Canadian First Nations is therefore an important resource for identifying plants that produce natural product drug leads. Bioassay screening of a library of thirty-five Canadian medicinal plant extracts identified eleven extracts that were potent inducers of apoptosis in an aggressive human breast carcinoma cell line (MDA-MB-231). Bioassay guided fractionation of the seven most active extracts ( Aralia nudicaulis , Juniperus communis , Nuphar lutea , Populus tremuloides , Hypericum perforatum , Moneses uniflora , and Orthilia secunda ) resulted in the isolation of thirteen natural products, most of which have not previously been reported to be pro-apoptotic. Although these results confirm the importance of our ethnopharmacological approach, our studies of medicinal plants has predominantly resulted in the isolation of known natural products. In addition to our medicinal plant extracts, we have conducted preliminary bioassay screening of a library of endophytic fungal extracts derived from medicinal plants. Our data indicate that these represent a promising source of pro-apoptotic natural products and, therefore, in the future we will be focussing our attention on these endophytes in an effort to discover novel chemical entities with anticancer activity. Acknowledgements: Hebelin Correa and Larry Calhoun are acknowledged for technical assistance.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".