Intervention of GI neuropeptides in pancreatic growth and regeneration: comparison with cholecystokinin.
Bibliographic record
Abstract
The pancreatic gland has an enormous potential for growth and regeneration, mainly in rodents. These processes remain mostly under the control of the GI hormone cholecystokinin (CCK). The human pancreas however does not show proliferative properties after partial pancreatectomy, but research in this field has been scarce. Recent studies indicate that CCK might not be the expected trophic agent since its two receptors CCK(A) and CCK(B) were not found on human exocrine pancreas. Therefore, if human pancreas grows and regenerates, it has to be under the influence of some unknown trophic factors. Neuropeptides receiving much attention lately as regulators of pancreatic functions could be among the searched trophic agents. This presentation focus on neuropeptides growth potential: GRP-Bombesin, GABA, PP, PYY, Neurotensin, SP, VIP, PACAP, CGRP and galanin. Some neuropeptides have moderate effects on pancreatic enzymes and electrolytes secretion: SP, VIP, PACAP. However, their trophic effects remain unexplored except for GRP-bombesin and PACAP. PACAP preferentially exhibits its mitogenic and proliferative effects on the pancreatic acinar cells AR4-2J via tyrosine kinase, phospholipase D and ornithine decarboxylase activation but not through adenylate cyclase. The growth promoting action of GRP-bombesin is well documented on rodent's pancreas. However, recent studies indicate that this neuropeptide is potentially trophic for larger mammals' pancreas. Indeed, investigators recently documented that bombesin induced pancreatic regeneration in the pig after partial pancreatectomy through mitogen-activated protein kinases activation as do CCK-8 and caerulein on rat pancreas. Have we found the magic pancreatic trophic factor in large mammals? Further investigations will tell.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".