Influence of a flooding dose of valine on key indicators of metabolic status in the growing pig
Bibliographic record
Abstract
A key concern with the flooding dose technique for measuring protein synthesis is that a large dose of amino acid (AA) can potentially change the animals' hormonal and nutritional status, which in turn can influence protein synthesis. Among stable isotope tracers, 1-[(13)C]-valine is the preferred AA for measuring protein synthesis in gut tissue and mucins. A study was conducted to determine the impact of a flooding dose of valine on the metabolic status of pigs. Six barrows [16.5 kg body weight (BW)] were randomly assigned to intravenous infusions of either 150 mM valine (1.5 mmol/kg BW) or physiological saline, following a crossover design. Blood samples were taken 10 min prior to infusion, at the end of infusion, at 10-min intervals for 60 min post-infusion, and at 90 and 120 min post-infusion. Plasma concentrations of insulin, glucose, AA, urea nitrogen and packed cell volume (PCV) were measured. Infusion of valine increased plasma valine concentrations (4129 vs. 582 μM; P < 0.05) but had no influence on PCV (26.4% vs. 27.2%) and plasma concentrations of glucose (6.0 vs. 5.8 mM) and insulin (8.2 vs. 8.5 μU/ml; P > 0.10). Plasma urea nitrogen concentration was reduced with valine infusion (8.5 vs. 7.8 mg/dl; P < 0.05). A flooding dose of valine had no impact on plasma concentrations of AA, and specifically branched-chain AA such as leucine (240 vs. 231 μM) and isoleucine (310 vs. 331 μM; P > 0.10). There was, however, a slight increase in the plasma concentrations of threonine (224 vs. 263 μM; P < 0.05) and a tendency towards reduced glycine (1387 vs. 1313 μM; P < 0.10). The results indicate that a flooding dose of valine does not cause a substantial change in the metabolic status of growing pigs and is therefore suitable for measuring protein synthesis rates.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".