NUPR1 works against the metabolic stress-induced autophagy-associated cell death in pancreatic cancer cells
Bibliographic record
Abstract
The incidence of pancreatic adenocarcinoma is increasing with more than 43,000 predicted new cases in the US and 65,000 in Europe this year. Pancreatic cancer patients have a short life expectancy with less than 3-4% 5-y survival, which results in an equivalent incidence and mortality rate. One of the major challenges in pancreatic cancer is the identification of pharmacological approaches that overcome the resistance of this cancer to therapy. Intensive research in the past decades has led to the classification of pancreatic cancers and the identification of the driver key genetic events. Despite the advances in understanding the molecular mechanisms responsible for pancreatic cancer pathogenesis, this knowledge had little impact on significantly improving the treatment for this dismal disease. In particular, we know today that the lack of therapeutic response in pancreatic cancer is due to the intrinsic high resistance of these tumors to chemotherapy and radiation, rather than to the inappropriate design of these therapeutic approaches. Thus, in order to ensure a better outcome for pancreatic cancer patients, there is a strong need for research focused on the mechanism that determines this resistant phenotype and the means that might drive enhanced response to therapy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".