Glucagon-like peptide-2 enhances intestinal epithelial barrier function of both transcellular and paracellular pathways in the mouse
Bibliographic record
Abstract
BACKGROUND AND AIMS: Glucagon-like peptide-2 (GLP-2) is a recently identified potent intestinotrophic factor. We have evaluated the effect of GLP-2 treatment on intestinal epithelial barrier function in mice. METHODS: CD-1 mice were injected subcutaneously with GLP-2 or a protease resistant analogue, h[Gly(2)]GLP-2, twice daily for up to 10 days. Saline injected mice served as controls. Jejunal segments were mounted in Ussing chambers. Tissue conductance was measured and unidirectional fluxes were determined for (i) Na(+) and the small inert probe Cr-EDTA (both transported via the paracellular pathway) and (ii) the macromolecule horseradish peroxidase (HRP, transported via the transcellular pathway). RESULTS: Mice treated with GLP-2 or h[Gly(2)]GLP-2 for 10 days demonstrated significantly reduced intestinal conductance and fluxes of Na(+), Cr-EDTA, and HRP. Electron microscopy confirmed that GLP-2 reduced endocytic uptake of HRP into enterocytes. Functional changes (evident by four hours) preceded morphological changes (evident by 48 hours). CONCLUSIONS: GLP-2 enhances intestinal epithelial barrier function by affecting both paracellular and transcellular pathways and thus may be of therapeutic value in a number of gastrointestinal conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".