<i>In Vitro</i> Cytotoxic Activity against Breast, Cervical, and Ovarian Cancer Cells and Flavonoid Content of Plant Ingredients Used in a Selected Thai Traditional Cancer Remedy: Correlation and Hierarchical Cluster Analysis
Bibliographic record
Abstract
This study aimed to investigate in vitro cytotoxic activity of selected plant ingredients from a traditional Thai remedy for the treatment of cancer patients against cancer cells occurring in women such as MCF‐7 (breast cancer), SKOV3 (ovarian cancer), and HeLa (cervical cancer) cell lines. The plants and the remedy were macerated with 95% ethanol and boiled in water. Cytotoxic activity of the extracts was analyzed by SRB assay. Total flavonoid contents of the extracts were determined and their correlation with cytotoxic activity was evaluated. The hierarchical cluster analysis (HCA) was used to classify the extracts by their cytotoxic characteristics. A total of 66.7% of the plants was active against the tested cancer cell lines. Among the 44 plants in the remedy used for cancer treatment, nine plants that are also used in Thai cuisine exerted significant cytotoxicity against tested cancer cell lines. Eleven plants in the remedy were active against at least one of the tested cancer cell lines. All extracts were grouped into three groups and illustrated as heat map and hierarchical dendrogram. Total flavonoid content showed weak or no correlation with cytotoxic activity. A. dahurica, F. albopurpurea, and T. indica selectively exerted potent cytotoxic activity against MCF‐7 with SI value more than 6. A. galanga, P. amarus , L. striatum , H. indicum, and F. vulgare exerted moderate cytotoxicity to all tested cell with low toxicity to normal cells. The correlation and HCA performed in this study provided an alternative way to investigate biological activities of plant ingredients in polyherbal traditional remedies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".