Selective Induction of Apoptosis and Autophagy Through Treatment With Dandelion Root Extract in Human Pancreatic Cancer Cells
Bibliographic record
Abstract
OBJECTIVES: Pancreatic cancer has a 100% mortality rate; the aim of this study is to evaluate the efficacy of dandelion root extract (DRE) in inducing apoptosis and autophagy in aggressive and resistant pancreatic cancer cells. METHODS: The effect of DRE was evaluated using WST-1 (4-[3-(4-iodophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate) assay. Apoptotic cell death was confirmed by nuclear condensation by Hoechst staining and externalization of phosphatidylserine to the outer leaflet of the plasma membrane by Annexin-V binding assay. Loss of mitochondrial membrane potential was observed using the JC-1 (5,5',6, 6'-tetrachloro-1,1',3,3' tetraethylbenzimidazolylcarbocyanine iodide) dye. The induction of autophagy was detected using a monodansylcadaverine assay and this was confirmed by immunofluorescence for light chain 3-II. RESULTS: BxPC-3 and PANC-1 pancreatic cells were sensitive to aqueous DRE. This extract induces selective apoptosis in a dose- and time-dependent manner. Dandelion root extract caused the collapse of the mitochondrial membrane potential, leading to prodeath autophagy. Normal human fibroblasts were resistant at similar doses. CONCLUSIONS: We demonstrate that DRE has the potential to induce apoptosis and autophagy in human pancreatic cancer cells with no significant effect on noncancerous cells. This will provide a basis on which further research in cancer treatment through DRE can be executed.
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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".