Cytotoxic Activity Against Liver Cancer and Cholangiocarcinoma Cells of Artemisia vulgaris L. Extract and Cirsimaritin, Its Isolated Compound
Bibliographic record
Abstract
Introduction: Liver cancer is the most common cancer in Thailand. Artemisia vulgaris L. is one of Thai herbs used in many Thai traditional cancer recipes. The objective was to investigate cytotoxic activity against hepatocellular carcinoma (HepG2), cholangiocarcinoma (KKU-M156) cells of Artemisia vulgaris L. Extract, and its compound. Methods: The SRB assay was used to determine the cytotoxic activity. Bioassay guided isolation was used for isolating cytotoxic compound. Results: The ethanolic extract of A. vulgaris L. showed good cytotoxic activity against HepG2 and KKU-M156 (IC50 =13.36 ± 0.45 and 17.07 ± 0.95 µg/mL, respectively), while it also showed cytotoxicity against normal cell lines (HaCaT, IC50 = 27.30 ± 3.71 µg/mL). Vacuum liquid chromatography (VLC) in (Chloroform: Methanol) fraction showed the best cytotoxic. Cirsimaritin, the isolated compound from the active fraction showed better cytotoxic activity against HepG2 than KKU-M156 (IC50 =1.82 ± 0.63 and 21.01 ± 0.84 µg/mL, respectively), while it had no cytotoxicity against normal cell lines (HaCaT) (IC50 >100 µg/mL). This compound showed selective cytotoxicity against HepG2 with selective index (SI) value of 54.94. Conclusions: Cirsimaritin showed selective cytotoxic activity against liver cancer cells with the IC50 value less than 4 µg/mL and the SI value more than 50. In regards to NCI criteria which consider that the value of IC50 of a good cytotoxic compound must be less than 4 µg/mL, therefore, it has the potential to be developed as an anti-cancer drug for liver cancer cells. It also demostrated selective cytotoxic activity against liver cancer cells better than cholangiocarcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".