Stephanine from <i>Stephania venosa</i> (Blume) Spreng Showed Effective Antiplasmodial and Anticancer Activities, the Latter by Inducing Apoptosis through the Reverse of Mitotic Exit
Bibliographic record
Abstract
Extracts from the tubers of Stephania venosa (Blum) Spreng growing in Vietnam significantly inhibited cell proliferation against a number of cancer cells including HeLa, MDA-MB231 and MCF-7 cells. A bioassay-guided fractionation led to the isolation of four aporphine and one tetrahydroprotoberberine alkaloids: dehydrocrebanine 1, tetrahydropalmatine 2, stephanine 3, crebanine 4 and O-methylbulbocapnine 5. The characterization of these compounds was based on MS, NMR and published data. A study by structure-bioactivity relationship on these isolates showed that stephanine is the most active compound. Cell biological studies showed that stephanine induces the reverse of mitotic exit, eventually leading to cell death by apoptosis. This data suggests that stephanine has a unique mode of cell-killing activity against cancer cells, which is seldom observed with known synthetic compounds. In addition to its anticancer property, our data from an in vitro study showed that S. venosa also possesses effective antiplasmodial activity and stephanine was also the most interesting compound but is the most cytotoxic with the lowest selectivity index. Copyright © 2017 Her Majesty the Queen in Right of Canada Phytotherapy Research StartCopTextCopyright © 2017 John Wiley & Sons, Ltd.
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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".