Cytotoxicity, Apoptosis and DNA Damage Induced by <i>Alpinia galanga</i> Rhizome Extract
Bibliographic record
Abstract
Alpinia galanga, or galangal, has been a popular condiment used in Thai and Asian cuisine for many years. However, relatively little is known of the potential beneficial or adverse health effects of this spice. This study was conducted to analyze the capacity of galangal extract to induce cytotoxicity and DNA damage in six different human cell lines including normal and p53-inactive fibroblasts, normal epithelial and tumour mammary cells and a lung adenocarcinoma cell line. We deliberately focused on treatment with the crude aqueous extract of galangal rhizomes, rather than compounds extracted into an organic solvent, to more closely reflect the mode of dietary consumption of galangal. The cell lines displayed a broad range of cytotoxicity. There was no evidence for preferential cytotoxicity of tumour cells, but there was an indication that p53-active cell lines may be more sensitive than their p53-inactive counterparts. The contribution of apoptosis to total cell killing was only appreciable after exposure to 300 microg/mL of extract. Apoptosis appeared to be independent of p53 expression. Exposure to as little as 100 microg/mL galangal extract generated a significant level of DNA single-strand breaks as judged by the single-cell gel electrophoresis technique (comet assay). The three major UV-absorbing compounds in the aqueous extract were identified by mass spectrometry as 1'-acetoxychavicol acetate and its deacetylated derivatives. However, when tested in A549 human lung adenocarcinoma cells, these compounds were not responsible for the cytotoxicity induced by the complete aqueous extract.
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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".