Silibinin sensitizes chemo-resistant breast cancer cells to chemotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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) | 0.003 | 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 it