Crocetin inhibits pancreatic cancer cell proliferation and tumor progression in a xenograft mouse model
Bibliographic record
Abstract
Crocetin, a carotenoid compound derived from saffron, has long been used as a traditional ancient medicine against different human diseases including cancer. The aim of the series of experiments was to systematically determine whether crocetin significantly affects pancreatic cancer growth both in vitro and/or in vivo. For the in vitro studies, first, MIA-PaCa-2 cells were treated with crocetin and in these sets of experiments, a proliferation assay using H(3)-thymidine incorporation and flow cytometric analysis suggested that crocetin inhibited proliferation. Next, cell cycle proteins were investigated. Cdc-2, Cdc-25C, Cyclin-B1, and epidermal growth factor receptor were altered significantly by crocetin. To further confirm the findings of inhibition of proliferation, H(3)-thymidine incorporation in BxPC-3, Capan-1, and ASPC-1 pancreatic cancer cells was also significantly inhibited by crocetin treatment. For the in vivo studies, MIA-PaCa-2 as highly aggressive cells than other pancreatic cancer cells used in this study were injected into the right hind leg of the athymic nude mice and crocetin was given orally after the development of a palpable tumor. The in vivo results showed significant regression in tumor growth with inhibition of proliferation as determined by proliferating cell nuclear antigen and epidermal growth factor receptor expression in the crocetin-treated animals compared with the controls. Both the in vitro pancreatic cancer cells and in vivo athymic nude mice tumor, apoptosis was significantly stimulated as indicated by Bax/Bcl-2 ratio. This study indicates that crocetin has a significant antitumorigenic effect in both in vitro and in vivo on pancreatic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".