Cytotoxic activity of nemorosone in human MCF-7 breast cancer cells
Bibliographic record
Abstract
Estrogen receptor (ER) antagonists have been widely used for breast cancer treatment; however, patients have increasingly shown resistance and sensitivity to the high toxicity of these drugs, and identification of novel targeted therapies is therefore required. To determine whether nemorosone, a polycyclic polyisoprenylated benzophenone isolated from floral resins of Clusia rosea Jacq. and Cuban propolis samples, exerts anticancer effects on human breast cancer cells, estrogen receptor positive (ERα+) MCF-7 and estrogen receptor negative (ERα-) MDA-MB-231 and LNCaP cells were used. Cells were treated with nemorosone alone or in association with 17β-estradiol (E2) or an ER antagonist, ICI 182,780, a selective ER downregulator that completely abrogates estrogen-sensitive gene transcription. Nemorosone inhibited the cell viability of ERα+ but not of ERα- cells. In MCF-7, nemorosone induced inhibition of cell growth by blocking the cell cycle in the G₀/G₁ phase. Moreover, the expression of pERK1/2 and pAkt, considered to be hallmarks of the nongenomic estrogen signalling pathway, were reduced in MCF-7 cells treated with nemorosone. All these effects were enhanced by ICI 182,780. However, nemorosone was not able to interfere with E2-induced Ca²(+) release. These findings suggest that nemorosone may have therapeutic application in the treatment of breast cancer because of its activity on ERα.
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) | 0.002 | 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".