Alanine Metabolism in the Perfused Rat Liver
Bibliographic record
Abstract
We have utilized [(15)N]alanine or (15)NH(3) as metabolic tracers in order to identify sources of nitrogen for hepatic ureagenesis in a liver perfusion system. Studies were done in the presence and absence of physiologic concentrations of portal venous ammonia in order to test the hypothesis that, when the NH(4)(+):aspartate ratio is >1, increased hepatic proteolysis provides cytoplasmic aspartate in order to support ureagenesis. When 1 mm [(15)N]alanine was the sole nitrogen source, the amino group was incorporated into both nitrogens of urea and both nitrogens of glutamine. However, when studies were done with 1 mm alanine and 0.3 mm NH(4)Cl, alanine failed to provide aspartate at a rate that would have detoxified all administered ammonia. Under these circumstances, the presence of ammonia at a physiologic concentration stimulated hepatic proteolysis. In perfusions with alanine alone, approximately 400 nmol of nitrogen/min/g liver was needed to satisfy the balance between nitrogen intake and nitrogen output. When the model included alanine and NH(4)Cl, 1000 nmol of nitrogen/min/g liver were formed from an intra-hepatic source, presumably proteolysis. In this manner, the internal pool provided the cytoplasmic aspartate that allowed the liver to dispose of mitochondrial carbamyl phosphate that was rapidly produced from external ammonia. This information may be relevant to those clinical situations (renal failure, cirrhosis, starvation, low protein diet, and malignancy) when portal venous NH(4)(+) greatly exceeds the concentration of aspartate. Under these circumstances, the liver must summon internal pools of protein in order to accommodate the ammonia burden.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".