Does GLP‐2 infusion reduce colon injury and improve protein nutritional status of piglets with colitis?
Bibliographic record
Abstract
Glucagon-like peptide 2 (GLP-2) is a gastrointestinal hormone with strong trophic effects in small intestinal mucosa. Our goal was to assess the therapeutic potential of hGLP-2 infusion in a piglet colitis model. Twenty female piglets were randomized into 2 groups: hGLP-2 (GLP-2(1–33) 10 nmol/kg in 0.1% human albumin infused iv from d3–d10) and Control (0.1% albumin). Both groups received a macronutrient-restricted diet (50% of requirement - NRC) by gastrostomy, and dextran sulfate twice daily (0.75 g/(kg.d) from d3–d10) to induce colitis. Body composition was measured by Dual-energy X-ray Absorptiometry at beginning and end of the study. The stable isotope tracer L-[ring-2H5]phenylalanine was infused for 6h on d10 to determine protein synthesis. hGLP-2 infusion increased total GLP-2 in plasma. Histological assessment showed no change in inflammation or tissue repair scores in either spiral or distal colon. Fractional protein synthesis (FSR) of colon and liver proteins was not affected. Similarly, there were no differences in either FSR of the total plasma protein pool, or concentrations of total plasma proteins, albumin or fibrinogen. Growth, body weight gain and body composition were also not different. In conclusion, hGLP-2 infusion at this dose did not attenuate colonic damage and had no effect on protein synthesis or nutritional status in growing piglets with colitis. (Supported by NSERC)
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".