Crocin Enhances Cisplatin-Induced Chemosensitivity in Human Cervical Cancer Cell Line
Bibliographic record
Abstract
Background: Anti-tumor effects of crocin have been investigated in different tumors; however, its precise molecular mechanism is not exactly elucidated. Objectives: In the present investigation, we have studied pro-apoptotic and anti-proliferative properties of crocin and cisplatin combination on cervical cancer cells. Methods: Cell viability and apoptosis assays were monitored by MTT, Hoechst 33258 staining methods, respectively. The expression levels of apoptotic related genes, including Bax, Bcl-2, p53 mRNA, and miR-365 were analyzed by quantitative reverse transcription- polymerase chain reaction (qRT-PCR). Using Western blot, we have also assessed the protein expression of the above apoptotic genes. Results: The results of this study demonstrated that the combination treatment of cells with crocin and cisplatin significantly reduced the proliferation of cancer cells and induced apoptosis. Furthermore, the Bax/Bcl-2 ratio and the mRNA level of p53 markedly increased in treated cells, whereas it decreased miR-365 expression, an upstream regulator of Bcl2 and Bax. Conclusions: Accordingly, crocin could be a potential candidate for more evaluations such as in vivo studies, since it shows a proper in vitro anticancer effect. It is also suggested that a combination of crocin and cisplatin could be applied as an effective and promising chemotherapy strategy.
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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) | 0.002 | 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".