Astragaloside IV enhanced carboplatin sensitivity in prostate cancer by suppressing AKT/NF-κB signaling pathway
Bibliographic record
Abstract
In our study, we explored the effect of astragaloside IV (AgIV) on carboplatin chemotherapy in prostate cancer cell lines in vitro and in vivo. Cell viability assay, colony formation assay, flow cytometry, Western blot, immunohistochemistry, immunofluorescence, and tumor xenograft growth assay were conducted. We found that AgIV significantly decreased the half-maximal inhibitory concentration of carboplatin in prostate cancer cell lines LNCap and PC-3. Moreover, AgIV enhanced the effect of carboplatin in suppressing colony formation and inducing cell apoptosis. A low-dose carboplatin treatment upregulated N-cadherin and Vimentin expression and downregulated E-cadherin expression, but this effect was abolished by combining with AgIV. Carboplatin treatment increased the levels of p-AKT and p-p65 and decreased p-IκBα, but AgIV treatment suppressed this. In addition, AgIV synergized with carboplatin to suppress tumor xenograft growth of PC-3 cells, and decreased pAKT and p-p65 levels in vivo. Our results suggested that AgIV enhanced carboplatin sensitivity in prostate cancer cell lines by suppressing AKT/NF-κB signaling, thus suppressed epithelial-mesenchymal transition induced by carboplatin. Our findings provided a new mechanism for AgIV in overcoming drug resistance of platinum-based chemotherapy and suggested a potential combination therapy of AgIV and carboplatin in prostate cancer.
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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) | 0.001 | 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".