Minireview: Glucagon-Like Peptides Regulate Cell Proliferation and Apoptosis in the Pancreas, Gut, and Central Nervous System
Bibliographic record
Abstract
Gut peptides exert diverse effects regulating satiety, gastrointestinal motility and acid secretion, epithelial integrity, and both nutrient absorption and disposal. These actions are initiated by activation of specific G protein-coupled receptors and may be mediated by direct or indirect effects on target cells. More recent evidence demonstrates that gut peptides, exemplified by glucagon-like peptides-1 and 2 (GLP-1 and GLP-2), directly regulate signaling pathways coupled to cell proliferation and apoptosis. GLP-1 receptor activation enhances beta-cell proliferation and promotes islet neogenesis via activation of pdx-1 expression. The proliferative effects of GLP-1 appear to involve multiple intracellular pathways, including stimulation of Akt, activation of protein kinase Czeta, and transactivation of the epidermal growth factor receptor through the c-src kinase. GLP-1 receptor activation also promotes cell survival in beta-cells and neurons via increased levels of cAMP leading to cAMP response element binding protein activation, enhanced insulin receptor substrate-2 activity and, ultimately, activation of Akt. These actions of GLP-1 are reflected by expansion of beta-cell mass and enhanced resistance to beta-cell injury in experimental models of diabetes in vivo. GLP-2 also promotes intestinal cell proliferation and confers resistance to cellular injury in a variety of cell types. Administration of GLP-2 to animals with experimental intestinal injury promotes regeneration of the gastrointestinal epithelial mucosa and confers resistance to apoptosis in an indirect manner via yet-to-be identified GLP-2 receptor-dependent regulators of mucosal growth and cell survival. These proliferative and antiapoptotic actions of GLP-1 and GLP-2 may contribute to protective and regenerative actions of these peptides in human subjects with diabetes and intestinal disorders, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.021 |
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".