Investigating the Anticancer Effects of Natural Health Products in Combination with Chemotherapeutics Against Triple-Negative Breast Cancer
Bibliographic record
Abstract
In 2019, over 26 000 Canadian women were diagnosed with breast cancer. Although conventional chemotherapies are effective, their mechanism of action is not specific to cancerous cells. Classic chemotherapeutics, such as taxol and cisplatin, have shown to target healthy cells, therefore they are not optimal for long-term usage. Natural health products (NHPs) are non-toxic, safe for consumption and are effective for a variety of different purposes, notably for their anti-cancer effects. Rosemary Extract (RE) and White Tea Extract (WTE) (or Salvia rosmarinus and Camellia sinensus respectively) are both NHPs with long histories in traditional herbal medicines. Previous studies suggest they have various medicinal properties. We have studied whether RE and WTE can selectively induce cell death in MDA-MB-231, a highly aggressive breast cancer cell line. We have further studied whether they can be administered in conjunction with common chemotherapeutics. Through qualitative and quantitative analysis, we have demonstrated that RE and WTE exhibit selective anti-cancer activity. Combining plant-based extracts with established chemotherapeutics may not only provide more effective treatment but will also reduce the toxicity associated with the latter. We found that the extracts succeeded in enhancing the anticancer effectiveness of taxol and cisplatin. This work further evaluated the mechanism by which RE and WTE induce cell death in breast-cancers. The research will later be extended to in-vivo trials, where the effects of SG are investigated on genetically modified mouse models. The findings provided have offered scientific validation showing NHPs are well-tolerated and effective forms of breast-cancer therapy.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".