Dietary<scp>l</scp>-arginine supplementation enhances intestinal development and expression of vascular endothelial growth factor in weanling piglets
Bibliographic record
Abstract
Oral administration of L-arginine has been reported to prevent gut disease in human infants. However, little is known about the effects of dietary arginine supplementation on intestinal development of weaned piglets. In the present study, twenty 21-d-old castrated piglets with 5·3 (SEM 0·13) kg body weight (BW) were weaned from sows, individually housed and randomly assigned to one of the two maize- and soyabean meal-based diets supplemented with 0 or 1% L-arginine. After consuming the diets for 7 d, six pigs were randomly selected from each group to obtain various tissues. Compared with control pigs, dietary supplementation with 1% L-arginine did not affect feed intake but enhanced (P<0·05) the relative weight of the small intestine (+33 %), daily BW gain (+38 %) and feed efficiency (+28 %). The villus height of the duodenum, jejunum and ileum in arginine-supplemented piglets was 21, 28 and 25% greater (P<0·05) than in the nonsupplemented control group. Arginine supplementation increased (P<0·05) protein levels for vascular endothelial growth factor(VEGF) in duodenal, jejunal and ileal mucosae by 14, 39 and 35 %, respectively. Compared with the control group, dietary supplementation with 1% L-arginine increased (P<0·05) plasma concentrations of arginine and insulin (+36 %), and decreased (P<0·05) plasma concentrations of cortisol (233 %), NH3 (221 %) and urea (219 %). These results indicate that arginine supplementation enhances intestinal growth, development and expression of VEGF in early-weaned pigs fed a maize- and soyabean meal-based diet. The findings may have important implications for neonatal pigs under stressful or diseased conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".