Bibliographic record
Abstract
Pancreatic cancer is considered the fifth leading cause of cancer deaths in Canada and one of the most fatal diseases in the world. Its definite underlying cause is still unidentified, and its actual cell of origin remains unclear. Unfortunately, most of the current research on the pancreas is focused on one disease only, namely diabetes with much less consideration for other pancreatic diseases. Diabetes has been extensively studied from a developmental aspect, and continues to attract the interest of numerous researchers. On the contrary, few accomplishments have been done to decode the developmental errors occurring in pancreatic cancer. It is therefore necessary to allocate more research resources to address this disease from a developmental aspect. This study provides a literature review of the pancreas concerning its anatomy and function, transcription factors and signaling pathways controlling its development, and the role of these signaling pathways in pancreatic cancer. The review provides distinct emphasis on three important aspects. First, a review of pancreas development is provided, with a focus on different transcription factors and signaling pathways involved in this process. Second, it addresses how the signaling pathways which play a role in pancreas development are the same signaling pathways that play a role in pancreatic cancer, additional emphasis is placed on describing the genetic alterations occurring in pancreatic cancer. Third, a methodology of approaching pancreatic cancer research from a developmental aspect is presented. Using an example of one gene, Anterior gradient 2 (Agr2), is highly expressed in pancreatic cancer in ductal cells only, and might play a role in ductal cell development of the pancreas. Thus, the main objective of this review is to provide a developmental framework for the analysis of pancreatic cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".