Frontiers in glucagon-like peptide-2: multiple actions, multiple mediators
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2) is a pleiotropic hormone that affects multiple facets of intestinal physiology, including growth, barrier function, digestion, absorption, motility, and blood flow. The mechanisms through which GLP-2 produces these actions are complex, involving unique signaling mechanisms and multiple indirect mediators. As clinical trials have begun for the use of GLP-2 in a variety of intestinal disorders, the elucidation of such mechanisms is vital. The GLP-2 receptor (GLP-2R) is a G protein-coupled receptor, signaling through multiple G proteins to affect the cAMP and mitogen-activated protein kinase pathways, leading to both proliferative and antiapoptotic cellular responses. The GLP-2R also demonstrates unique mechanisms for receptor trafficking. Expression of the GLP-2R in discrete sets of intestinal cells, including endocrine cells, subepithelial myofibroblasts, and enteric neurons, has led to the hypothesis that GLP-2 acts indirectly through multiple mediators to produce its biological effects. Indeed, several studies have now provided important mechanistic data illustrating several of the indirect pathways of GLP-2 action. Thus, insulin-like growth factor I has been demonstrated to be required for GLP-2-induced crypt cell proliferation, likely involving activation of beta-catenin signaling. Furthermore, vasoactive intestinal polypeptide modulates the actions of GLP-2 in models of intestinal inflammation, while keratinocyte growth factor is required for GLP-2-induced colonic mucosal growth and mucin expression. Finally, enteric neural GLP-2R signaling affects intestinal blood flow through a nitric oxide-dependent mechanism. Determining how GLP-2 produces its full range of biological effects, which mediators are involved, and how these mediators interact is a continuing area of active research.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".