GLP‐2 infusion increases endogenous arginine synthesis in parenterally‐fed piglets receiving an arginine deficient diet
Bibliographic record
Abstract
Neonatal piglets fed via total parenteral nutrition (TPN) cannot synthesize sufficient arginine (ARG) to maintain ARG status, presumably due to the intestinal atrophy that occurs with TPN feeding. Glucagon‐like peptide 2 (GLP‐2) helps maintain intestinal structure during TPN feeding by reducing intestinal apoptosis and proteolysis and maintaining intestinal blood flow. GLP‐2 infusion was hypothesized to increase the rate of endogenous ARG synthesis from proline (PRO), the major ARG precursor, in TPN‐fed piglets receiving an ARG deficient diet. Male piglets (n=10, ~1.7 kg), fitted with jugular vein catheters for diet and isotope infusion, and femoral vein catheters for blood sampling (d 0), were allocated to a continuous infusion (1 mL/kg/h) of either GLP‐2 (n =5; 0.4 nmol/mL) or 0.9% saline (n =5) into the jugular vein catheter for 7 d. Piglets received 2 d of complete TPN, followed by 5 d of an ARG deficient (0.60 g/kg/d) TPN. Piglets received primed, constant infusions of [guanido‐ 14 C]ARG to measure ARG flux (d 6), and [U‐ 14 C]PRO (d 7) to measure PRO conversion to ARG. Plasma ARG concentrations and ARG fluxes were similar (P > 0.05). Piglets receiving GLP‐2 had a greater jejunal mucosal mass (P = 0.003) and a two‐fold greater rate of ARG synthesis (μ mol/kg/h) from PRO (11.6 vs. 6.3) (P = 0.03). Maximal ARG synthesis in TPN‐fed piglets is usually ~25% of that in enterally‐fed piglets. This study is the first to quantitate ARG synthesis in TPN‐fed neonates and show that GLP‐2 infusion increases ARG synthesis. Also, second‐pass intestinal metabolism may be important for ARG synthesis in TPN‐fed neonates. Funded by NSERC.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".