The therapeutic potential of vitamins A, C, and D in pancreatic cancer
Bibliographic record
Abstract
The pancreatic ductal adenocarcinoma (PDAC) is among the deadliest tumor diseases worldwide. While treatment options have generally become more diverse, little progress has been made in the treatment of PDAC and the median survival time for patients with locally advanced PDAC is between 8.7 and 13.7 months despite treatment. The aim of this review was to explore the therapeutic potential of complementing standard therapy with natural or synthetic forms of vitamins A, C, and D. The therapeutic use of vitamins A, C, and D could be a promising addition to the treatment of PDAC. For all three vitamins and their derivatives, tumor cell-specific cytotoxicity and growth inhibition against PDAC cells has been demonstrated in vitro and in preclinical animal models. While the antitumor effect of vitamin C is probably mainly due to its pro-oxidative effect in supraphysiological concentrations, vitamin A and vitamin D exert their effect by activating nuclear receptors and influencing gene transcription. In addition, there is increasing evidence that vitamin A and vitamin D influence the tumor stroma, making the tumor tissue more accessible to other therapeutic agents. Based on these promising findings, there is a high urgency to investigate vitamins A, C, and D in a clinical context as a supplement to standard therapy in PDAC. Further studies are needed to better understand the exact mechanism of action of the individual compounds and to develop the best possible treatment regimen. This could contribute to the long-awaited progress in the treatment of this highly lethal tumor entity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".