Silibinin sensitizes chemo-resistant breast cancer cells to chemotherapy
Bibliographic record
Abstract
CONTEXT: Multiple drug resistance is the major obstacle to conventional chemotherapy. Silibinin, a nontoxic naturally occurring compound, has anticancer activity and can increase the cytotoxic effects of chemotherapy in various cancer models. OBJECTIVE: To evaluate the effects of silibinin on enhancing the sensitivity of chemo-resistant human breast cell lines to doxorubicin (DOX) and paclitaxel (PAC). MATERIALS AND METHODS: The cells were treated with silibinin (at 50 to 600 μM concentrations) and/or chemo drugs for 24 and 48 h, then cell viability and changes in oncogenic proteins were determined by MTT assay and Western blotting/RT-PCR, respectively. Flow cytometry was used to study apoptosis in the cells receiving different treatments. The antitumorigenic effects of silibinin (at 200 to 400 μM concentration) were evaluated by mammosphere assay. RESULTS: from 71 to 10 μg/mL and significantly suppressed the key oncogenic pathways including STAT3, AKT, and ERK in these cells. Interestingly treatment of DOX-resistant MDA-MB-435 cells with silibinin at 400 μM concentration for 48 h induced a 50% decrease in the numbers of colonies as compared with DMSO-treated cells. Treatment of PAC-resistant MCF-7 cells with silibinin at 400 μM concentration generated synergistic effects when it was used in combination with PAC at 250 nM concentration (CI = 0.81). CONCLUSION: Silibinin sensitizes chemo-resistant cells to chemotherapeutic agents and can be useful in treating breast cancers.
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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 | 0.000 |
| 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".