Glucagon-like peptide 2 has limited efficacy to increase nutrient absorption in fetal and preterm pigs
Bibliographic record
Abstract
Exogenous glucagon-like peptide 2 (GLP-2) prevents intestinal atrophy and increases nutrient absorption in term newborn pigs receiving total parenteral nutrition (TPN). We tested the hypothesis that the immature intestine of fetuses and preterm neonates has a diminished nutrient absorption response to exogenous GLP-2. This was accomplished using catheterized fetal pigs infused for 6 days (87-91% of gestation) with GLP-2 (25 nmol.kg(-1).day(-1) iv; n = 7) or saline (n = 7), and cesarean-delivered preterm pigs (92% of gestation) that received TPN with GLP-2 (25 nmol.kg(-1).day(-1) iv; n = 8) or saline (n = 7) for 6 days after birth. Responses to GLP-2 were assessed by measuring intestinal dimensions, absorption of nutrients (glucose, leucine, lysine, proline) by intact tissues and brush border membrane vesicles, and abundance of sodium-glucose cotransporter mRNA. Infusion of GLP-2 increased circulating GLP-2 levels in fetuses, but did not increase intestinal mass or absorption of nutrients by intact tissues and brush border membrane vesicles, except for lysine. Administration of exogenous GLP-2 to preterm TPN-fed pigs similarly did not increase rates of nutrient absorption, yet nutrient absorption capacities of the entire small intestine tended to increase (+10-20%, P < 0.10) compared with TPN alone due to increased intestinal mass (+30%, P < 0.05). GLP-2 infusion did not increase sodium-glucose cotransporter-1 mRNA abundance in fetuses or postnatal preterm pigs. Hence, the efficacy of exogenous GLP-2 to improve nutrient absorption by the intestine of fetal and preterm pigs is limited compared with term pigs and more mature animals and humans.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".