Glucagon‐like Peptide‐2 and the Regulation of Intestinal Growth and Function
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2) is an intestinally derived hormone that enhances intestinal growth, digestion, absorption, barrier function, and blood flow in healthy animals as well as preventing damage and improving repair in preclinical models of enteritis and colitis and following massive small bowel resection. These beneficial effects of GLP-2 on the intestinal tract are largely recapitulated in humans with intestinal failure. The high-specificity of this peptide for the intestinal tract and the development of degradation-resistant, long-acting GLP-2 receptor agonists have rapidly led to clinical implementation of GLP-2-based therapy for the treatment of patients with short bowel syndrome, with few reported side effects. This comprehensive review covers the biology of GLP-2, from the control of proglucagon gene expression and the posttranslational processing of proglucagon to liberate GLP-2 to the regulation of GLP-2 secretion from the intestinal L cell, and from the mechanism of action of GLP-2 through its highly localized receptor to the biological activities of GLP-2 in the intestine and other restricted locations in the body, under physiological conditions as well as in animal models of intestinal disease and in patients with short bowel syndrome. Collectively, the history of GLP-2 serves as a remarkable bench-to-bedside story of translational medicine. © 2017 American Physiological Society. Compr Physiol 8:1185-1210, 2018.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".