Nutrient-stimulated GLP-2 release and crypt cell proliferation in experimental short bowel syndrome
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2) is an enteroendocrine peptide that is released in response to luminal nutrients and has unique trophic actions in the gastrointestinal tract. These features suggest GLP-2 may be important in controlling intestinal adaptation. We examined the relationship over time of GLP-2 production and adaptation to intestinal resection, the effects of resection-induced malabsorption on GLP-2 production, and the correlation of endogenous serum GLP-2 levels with adaptation as measured by crypt-cell proliferation (CCP). We initially examined the effect of nutrient malabsorption, induced by a 90% resection of the proximal intestine studied on day 4, on the time course and levels of GLP-2 release. Secondly, the degree of malabsorption was varied by performing intestinal transection or 50, 75, or 90% resection of proximal small intestine. Finally, the relationship of GLP-2 levels over time with adaptation to a 90% resection was examined by determining GLP-2 levels on days 7, 14, and 28, and correlating this with intestinal adaptation, as assessed by morphology and CCP rate. A 90% resection significantly increased basal and postprandial GLP-2 levels, with a net increase in nutrient-stimulated exposure over 90 min; GLP-2 exposure (integrated levels vs. time) increased 12.7-fold in resected animals (P < 0.001). Basal and postprandial GLP-2 levels significantly correlated with the magnitude of intestinal resection (r(2) = 0.71; P < 0.001), CCP (r(2) = 0.48; P < 0.005), and nutrient malabsorption (protein, P < 0.001; fat, P < 0.005). The increase in CCP was maintained to 28 days after small bowel resection and was associated with an ongoing elevation in GLP-2 release. These findings suggest that GLP-2 is important in initiating and maintaining the small intestinal adaptive response to resection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".