Potentiating effect of β-caryophyllene on anticancer activity of α-humulene, isocaryophyllene and paclitaxel
Bibliographic record
Abstract
beta-caryophyllene is a sesquiterpene widely distributed in essential oils of various plants. Several biological activities are attributed to beta-caryophyllene, such as anti-inflammatory, antibiotic, antioxidant, anticarcinogenic and local anaesthetic activities. In this work, the potentiating effect of beta-caryophyllene on the anticancer activity of alpha-humulene, isocaryophyllene and paclitaxel against MCF-7, DLD-1 and L-929 human tumour cell lines was evaluated. A non-cytotoxic concentration of beta-caryophyllene significantly increased the anticancer activity of alpha-humulene and isocaryophyllene on MCF-7 cells: alpha-humulene or isocaryophyllene alone (32 microg mL(-1)) inhibited cell growth by about 50% and 69%, respectively, compared with 75% and 90% when combined with 10 microg mL(-1) beta-caryophyllene. Moreover, beta-caryophyllene potentiated the anticancer activity of paclitaxel on MCF-7, DLD-1 and L-929 cell lines. The highest potentiating effect was obtained in DLD-1 cells treated with paclitaxel combined with 10 microg mL(-1) beta-caryophyllene, which increased the paclitaxel activity about 10-fold. The intracellular accumulation of paclitaxel-oregon green was evaluated in combination with concentrations of beta-caryophyllene ranging from 2.5 to 40 microg mL(-1). beta-Caryophyllene (10 microg mL(-1)) significantly increased the intracellular accumulation of paclitaxel-oregon green (about 64% over controls). Moreover, beta-caryophyllene induced intracellular accumulation of calcein but not verapamil, an inhibitor of P-glycoprotein and multidrug resistance related protein transporters, suggesting that beta-caryophyllene promotes drug accumulation by a different mechanism of action. These results suggest that beta-caryophyllene facilitates the passage of paclitaxel through the membrane and thus potentiates its anticancer activity.
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 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".