Evaluating Metformin as a Potential Chemosensitizing Agent when Combined with Docetaxel Chemotherapy in Castration-resistant Prostate Cancer Cells
Bibliographic record
Abstract
BACKGROUND/AIM: Docetaxel, the first-line chemotherapy for metastatic castration-resistant prostate cancer (mCRPC), provides certain survival benefits, but is associated with significant toxicity. A novel therapeutic approach for mCRPC is combining docetaxel with a chemosensitizing agent. We hypothesized that metformin, a potential chemosensitizer, would improve docetaxel efficacy in CRPC cells. MATERIALS AND METHODS: MTS assays were used to determine the effect of metformin-docetaxel treatment on PC3 and DU145 cell viability. Wound-healing and ATP concentration assays were used to evaluate cell migration and intracellular ATP levels following metformin-docetaxel treatment. Western blotting was used for mechanistic evaluation. RESULTS: Metformin-docetaxel treatment significantly reduced PC3 cell viability. Metformin-docetaxel treatment did not significantly affect cell migration or intracellular ATP levels. Western blotting revealed metformin-docetaxel treatment did not significantly change AMPK or P-AMPK expression patterns. CONCLUSION: Metformin may be an effective chemosensitizer for certain types of CRPC cells, but further investigation is needed.
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 | 0.001 |
| 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".