Data from Mechanisms of Ascorbate-Induced Cytotoxicity in Pancreatic Cancer
Bibliographic record
Abstract
Abstract Purpose: Pharmacologic concentrations of ascorbate may be effective in cancer therapeutics. We hypothesized that ascorbate concentrations achievable with i.v. dosing would be cytotoxic in pancreatic cancer for which the 5-year survival is <3%. Experimental Design: Pancreatic cancer cell lines were treated with ascorbate (0, 5, or 10 mmol/L) for 1 hour, then viability and clonogenic survival were determined. Pancreatic tumor cells were delivered s.c. into the flank region of nude mice and allowed to grow at which time they were randomized to receive either ascorbate (4 g/kg) or osmotically equivalent saline (1 mol/L) i.p. for 2 weeks. Results: There was a time- and dose-dependent increase in measured H2O2 production with increased concentrations of ascorbate. Ascorbate decreased viability in all pancreatic cancer cell lines but had no effect on an immortalized pancreatic ductal epithelial cell line. Ascorbate decreased clonogenic survival of the pancreatic cancer cell lines, which was reversed by treatment of cells with scavengers of H2O2. Treatment with ascorbate induced a caspase-independent cell death that was associated with autophagy. In vivo, treatment with ascorbate inhibited tumor growth and prolonged survival. Conclusions: These results show that pharmacologic doses of ascorbate, easily achievable in humans, may have potential for therapy in pancreatic cancer. Clin Cancer Res; 16(2); 509–20
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".