Green Tea Leaves and Rosemary Extracts Selectively Induce Cell Death in Triple-Negative Breast Cancer Cells and Cancer Stem Cells and Enhance the Efficacy of Common Chemotherapeutics
Bibliographic record
Abstract
While incredible medical advancements in chemotherapeutics development for cancer treatment have been made, the majority of these are not selective in their mechanism of action, leading to adverse effects. Given the systemic toxicity associated with these therapies, they are not well suited for long-term use. Natural health products, or NHPs, may provide a way to selectively target the oxidative and metabolic vulnerabilities in cancer cells. White tea (Camelia sinensis) and rosemary (Salvia rosmarinus) are two natural extracts that have been studied extensively for their medicinal properties. However, their anticancer activity and mechanism of action are yet to be fully elucidated. We have examined the extracts’ cancer cell-killing ability as well as their interactions with common chemotherapeutics in MDA-MB-231 cells, a triple-negative breast cancer cell line, in vitro. Cell death measurement, morphological and biochemical characterization of apoptotic cell death, mechanisms of action (mitochondrial depolarization and oxidative stress), and immunofluorescence assays to estimate the percentage of cancer stem cells (CSCs) were performed following treatment with Synthite tea extract (STE) and rosemary extract (RE), provided by Synthite Industries Limited alone and in combination with cisplatin and paclitaxel. The key findings in this study are that STE and RE alone demonstrated very efficient anticancer activity against TNBC, and more importantly, the administration of the extracts in conjunction with cisplatin and paclitaxel sensitizes cancer cells to achieve enhanced cell death. In addition, CSCs were found to be sensitive to treatment with STE alone and in combination with RE and exhibited greater sensitivity to combination therapies compared to chemotherapeutic alone. The significance of these observations is that STE and RE, well-tolerated NHPs, have the potential to enhance the efficacy of current chemotherapeutics when combined, as well as prevent relapse for TNBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".