The Efficacy of Dandelion Root Extract in Inducing Apoptosis in Drug‐Resistant Human Melanoma Cells
Bibliographic record
Abstract
Notoriously chemoresistant melanoma has become the most prevalent form of cancer for the 25-29 North American age demographic. Standard treatment after early detection involves surgical excision (recurrence is possible), and metastatic melanoma is refractory to immuno-, radio-, and most harmful chemotherapies. Various natural compounds have shown efficacy in killing different cancers, albeit not always specifically. In this study, we show that dandelion root extract (DRE) specifically and effectively induces apoptosis in human melanoma cells without inducing toxicity in noncancerous cells. Characteristic apoptotic morphology of nuclear condensation and phosphatidylserine flipping to the outer leaflet of the plasma membrane of A375 human melanoma cells was observed within 48 hours. DRE-induced apoptosis activates caspase-8 in A375 cells early on, demonstrating employment of an extrinsic apoptotic pathway to kill A375 cells. Reactive Oxygen Species (ROS) generated from DRE-treated isolated mitochondria indicates that natural compounds in DRE can also directly target mitochondria. Interestingly, the relatively resistant G361 human melanoma cell line responded to DRE when combined with the metabolism interfering antitype II diabetic drug metformin. Therefore, treatment with this common, yet potent extract of natural compounds has proven novel in specifically inducing apoptosis in chemoresistant melanoma, without toxicity to healthy cells.
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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".