Supplementation of pre-weaning diet with <scp>l</scp>-arginine has carry-over effect to improve intestinal development in young piglets
Bibliographic record
Abstract
This study tested the hypothesis that pre-weaning supplemental arginine may have a carry-over effect on intestinal growth and development of piglets immediately after weaning. Fifty-four [Duroc × (Landrace × Yorkshire)] piglets were fed a milk replacer diet supplemented with 0 (control), 4, or 8 g kg−1 of l-arginine from d 4 to 21 of age (6 replicate pens of 3 piglets per group). Piglets were then weaned to a common corn–soybean meal diet and fed for another 21 d. On day 42, 6 pigs per treatment were randomly selected for blood and tissue sampling. Arginine supplementation improved body weight of the piglets on d 42, average daily gain during d 22–31 (P < 0.05). Supplementation of 8 g kg−1 arginine decreased feed:gain (F:G) ratio in piglets during d 22–31 (P = 0.010). Compared with controls, 8 g kg−1 arginine improved villous height in duodenum, jejunum, and ileum; villous area in duodenum and jejunum; relative intestine weight; and plasma contents of insulin at d 42 (P < 0.05). Arginine supplementation increased mucosal protein content in all 3 segments of the small intestine (P < 0.05). These novel results clearly demonstrate a carry-over effect of pre-weaning supplementation with arginine on enhanced intestinal growth and development in the early post-weaning period.
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) | 0.001 | 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".