Arginine synthesis does not occur during first-pass hepatic metabolism in the neonatal piglet
Bibliographic record
Abstract
We have shown that first-pass intestinal metabolism is necessary for approximately 50% of whole body arginine synthesis from its major precursor proline in neonatal piglets. Furthermore, the intestine is not the site of increased arginine synthesis observed during dietary arginine deficiency. Primed constant intravenous (iv) and intraportal (ip) infusions of L-[U-14C]proline, and iv infusion of either L-[guanido-14C]arginine or L-[4,5-3H]arginine were used to measure first-pass hepatic arginine synthesis in piglets enterally fed either deficient (0.20 g.kg(-1).day(-1)) or generous (1.80 g.kg(-1).day(-1)) quantities of arginine for 5 days. Conversion of arginine to other urea cycle intermediates and arginine recycling were also calculated for both dietary treatments. Arginine synthesis (g.kg(-1).day(-1)) from proline was greater in piglets (P < 0.05) fed the deficient arginine diet in both the presence (generous: 0.07; deficient: 0.17; pooled SE = 0.01) and absence (generous: 0.06; deficient: 0.20; pooled SE = 0.01) of first-pass hepatic metabolism. There was no net arginine synthesis from proline during first-pass hepatic metabolism regardless of arginine intake. Arginine conversion to urea, citrulline, and ornithine was significantly greater (P < 0.05) in piglets fed the generous arginine diet. Calculated arginine fluxes were significantly lower (P = 0.01) for [4,5-3H]arginine than for [guanido-14C]arginine, and the discrepancy between the values was greater in piglets fed the deficient arginine diet (35% vs. 20%). Collectively, these findings show that first-pass hepatic metabolism is not a site of net arginine synthesis and that piglets conserve dietary arginine in times of deficiency by decreasing hydrolysis and increasing recycling.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".