Evaluating the cytotoxic effects of the water extracts of four anticancer herbs against human malignant melanoma cells
Bibliographic record
Abstract
Abstract: Malignant melanoma (MM) is the most dangerous type of skin cancer, killing more than 1,100 people each year in Canada. Prognosis for late stage and recurrent MM is extremely poor due to insensitivity to chemotherapy drugs, and thus many patients seek complementary and alternative medicines. In this study, we examined four commonly used anticancer herbs in traditional Chinese medicine, Hedyotis diffusa , Scutellaria barbata , Lobelia chinensis , and Solanum nigrum , for their in vitro antitumor effects toward human MM cell line A-375. The crude water extract of S. nigrum (1 g of dry herb in 100 mL water) and its 2-fold dilution caused 52.8%±13.0% and 17.3%±2.7% cytotoxicity in A-375 cells, respectively ( P <0.01). The crude water extract of H. diffusa caused 11.1%±12.4% cytotoxicity in A-375 cells with no statistical significance ( P >0.05). Higher concentrated formulation might be needed for H. diffusa to exert its cytotoxic effect against A-375 cells. No cytotoxicity was observed in A-375 cells treated with crude water extract of S. barbata and L. chinensis . Further high performance liquid chromatography-tandem mass spectroscopy analysis of the herbal extracts implicated that S. nigrum and H. diffusa might have adopted the same bioactive components for their cytotoxic effects in spite of belonging to two different plant families. We also showed that the crude water extract of S. nigrum reduced intracellular reactive oxygen species generation in A-375 cells, which may lead to a cytostatic effect. Furthermore, synergistic effect was achieved when crude water extract of S. nigrum was coadministered with temozolomide, a chemotherapy drug for skin cancer. Keywords: herbal extract, malignant melanoma, cytotoxicity, HPLC-MS/MS, reactive oxygen species
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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".