Dandelion root and lemongrass extracts induce apoptosis, enhance chemotherapeutic efficacy, and reduce tumour xenograft growth in vivo in prostate cancer
Bibliographic record
Abstract
Dandelion root and lemongrass extracts induce apoptosis, enhance chemotherapeutic efficacy, and reduce tumour xenograft growth in vivo in prostate cancer Christopher Nguyen1, Ali Mehaidli1, Kiruthika Baskaran1, Sahibjot Grewal1, Alaina Pupulin1, Ivan Ruvinov1, Benjamin Scaria1, Krishan Parashar1, Caleb Vegh1, Siyaram Pandey1* 1Department of Chemistry and Biochemistry, University of Windsor, Windsor, Ontario N9B 3P4, Canada Many conventional chemotherapies have indicated side effects due to a lack of treatment specificity and are thus not suitable for long-term usage. Natural health products are well-tolerated and safe for consumption, and some have pharmaceutical uses particularly for their anti-cancer effects. The anti-cancer efficacy of dandelion (Taraxacum officinale) root and lemongrass (Cymbopogon citratus) extracts on prostate cancer and their interactions with standard chemotherapeutics has not been studied to determine if they will be suitable for adjuvant therapies. If successful, these extracts could potentially be used in conjunction with chemotherapeutics to minimize the risk of drug-related toxicity and enhance the efficacy of the treatment. We have demonstrated that both dandelion root extract (DRE) and lemongrass extract (LGE) exhibit selective anti-cancer activity. Importantly, DRE and LGE addition to the chemotherapeutics taxol and mitoxantrone was determined to enhance the induction of apoptosis when compared to individual chemotherapy treatment alone. Further, DRE and LGE were able to significantly reduce the tumour burden in prostate cancer xenograft models when administered orally, while also being well-tolerated. Thus, the implementation of these well-tolerated extracts in adjuvant therapies could be a selective and efficacious approach to prostate cancer treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".