Proline, in a commercial total parenteral nutrition (TPN) solution, is inadequate in meeting the metabolic requirements of the parenterally‐fed neonatal piglet
Bibliographic record
Abstract
Mammalian milk contains large amounts of proline (73 to 106 mg/g total amino acid). Some TPN solutions contain less than milk would provide. These solutions do not appear to have been specifically tested for proline adequacy in vivo. Recent studies, using the neonatal piglet as a model for the human infant, found that a TPN solution modelled on a commercial product may not contain sufficient proline (2.8g/100g, 0.43g/kg*d), despite apparently adequate arginine. Male neonatal piglets (n=5, ~1.8kg) were implanted with a jugular catheter for diet and isotope infusion and femoral catheter for blood sampling. They received a TPN solution with an amino acid pattern similar to a commercial (control) product and the same solution supplemented with proline (PRO+; 8.2g/100g, 1.25g/kg*d). On d5, piglets received either the control or PRO+ diets for 24h, followed by a primed, constant infusion of 14 C phenylalanine (PHE) for 4h. On d6 piglets received the other diet and underwent another infusion on d7. Breath and blood samples were taken to measure PHE flux, oxidation and plasma amino acid concentrations. Plasma proline concentrations (μmol/L) increased significantly (P<0.01) when the diet was supplemented with proline, bringing the plasma concentration within the sow‐fed reference range. PRO+ reduced the percent dose of PHE oxidized in all piglets yet no significance was found. These results suggest that the proline concentration of this TPN solution was inadequate in meeting the metabolic requirements of the neonatal piglet.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".