p14ARF upregulation of p53 and enhanced effects of 5-fluorouracil in pancreatic cancer.
Bibliographic record
Abstract
OBJECTIVE: To investigate the synergistic antitumor effects of combined use of p14ARF gene and 5-fluorouracil (5-Fu) in pancreatic cancer. METHODS: A human pancreatic cancer cell line PC-3 was transfected with lipofectin-mediated recombinant p14ARF gene, and was then administered with 5-Fu. Cell growth, morphological changes, cell cycle, apoptosis, and molecular changes were measured using the MTT assay, flow cytometry, RT-PCR, Western blotting, and immunocytochemical assays. RESULTS: After transfection of p14ARF, cell growth was obviously inhibited, resulting in an accumulation of cells in the G(1) phase. The proportion of cells in the G(1) phase was significantly increased from 58.51% to 75.92%, and in the S and G(2)/M phases decreased significantly from 20.05% to 12.60%, and from 21.44% to 11.48%, respectively, as compared with those of the control groups. PC-3/p14ARF cells that underwent 5-Fu treatment had significantly greater G(2)/M phase accumulation, from 11.48% to 53.47%. The apoptopic index was increased in PC-3/p14ARF cells from 3.64% to 19.62%. The MTT assay showed p14ARF-expressing cells were significantly more sensitive to 5-Fu (0.01 - 10 mg/L) than those devoid of p14ARF expression (P < 0.01). Western blotting showed p14ARF upregulates p53 expression. CONCLUSION: Combined use of p14ARF gene and 5-Fu acts synergistically to inhibit pancreatic cancer cell proliferation, suggesting a new anticancer 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 | 0.000 |
| 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".