Promoting ectopic pancreatic fates: pancreas development and future diabetes therapies
Bibliographic record
Abstract
Diabetes is a disease that could be treated more effectively with a better understanding of pancreas development. This review examines the role of master regulator genes driving crucial steps in pancreas development, from foregut specification to differentiation of the five endocrine cell types. The roles of Pdx1, Ptf1a, and Ngn3 are particularly examined as they are both necessary and sufficient for promoting pancreatic cell fates (Pdx1, Ptf1a) and endocrine cell development (Ngn3). The roles of Arx and Pax4 are studied as they compose part of the regulatory mechanism balancing development of different types of endocrine cells within the iselts and promote the development of alpha/PP and beta/delta cell progenitors, respectively. The roles of the aforementioned genes, and the consequences of misexpression of them for functionality of the pancreas, are examined through recent studies in model organisms, particularly Xenopus and zebrafish. Recent developments in cell replacement therapy research are also covered, concentrating on stem cell research (coaxing both adult and embryonic stem cells toward a beta cell fate) and transdifferentiation (generating beta cells from other differentiated cell types).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".