Addition of Gallic Acid Overcomes Resistance to Cisplatin in Ovarian Cancer Cell Lines
Bibliographic record
Abstract
OBJECTIVE: Ovarian cancer is one of the leading causes of cancer-related mortality in women, and is often associated with drug resistance. Therefore, finding effective drugs, including naturally derived compounds, is urgently needed. Herein, we aimed to test the anti-cancer potential of gallic acid monohydrate (GA) and its congeners on cisplatin-sensitive (A2780S), and resistant (A2780CP) ovarian cancer and normal ovarian (HOSE6-3) cell lines. METHODS: Cytotoxicity was assessed by AlamarBlue and CCK08 assays by exposing cells to different concentrations of cisplatin (0-21µg/mL), GA and its congeners (0-100µg/mL), and a combination of GA and cisplatin. Apoptosis was estimated by Hoechst stain and monitoring the relative RNA expression of the apoptotic effector caspase-3 using qRT-PCR. RESULTS: GA decreased cell viability in a concentration-dependent manner in all cell lines, with an IC50 of 19.39µg/mL (A2780S), 35.59 µg/mL (A2780CP), and 49.32µg/mL (HOSE6-3). GA displayed higher cytotoxicity than its congeners. An apoptotic rate estimation of approximately 20% and 30% was obtained in A2780S and A2780CP. While the cytotoxicity observed with cisplatin and GA was comparable, combining the two enhanced the cytotoxicity significantly, especially in the A2780CP cell line (p<0.05). CONCLUSION: These data suggest that GA may help overcome the resistance. Hence, the cytotoxic effects of GA, especially on chemo-resistant ovarian cancer cells merit further investigation. .
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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 | 0.001 |
| 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".