Effect of protein restriction on<sup>15</sup>N transfer from dietary [<sup>15</sup>N]alanine and [<sup>15</sup>N]<i>Spirulina platensis</i>into urea
Bibliographic record
Abstract
Six normal men consumed a mixed test meal while adapted to high (1.5 g. kg(-1) x day(-1)) and low (0.3 g. kg(-1) x day(-1)) protein intakes. They completed this protocol twice: when the test meals included 3 mg/kg of [(15)N]alanine ([(15)N]Ala) and when they included 30 mg/kg of intrinsically labeled [(15)N]Spirulina platensis ([(15)N]SPI). Six subjects with insulin-dependent diabetes mellitus (IDDM) receiving conventional insulin therapy consumed the test meal with added [(15)N]Ala while adapted to their customary high-protein diet. Protein restriction increased serum alanine, glycine, glutamine, and methionine concentrations and reduced those of leucine. Whether the previous diet was high or low in protein, there was a similar increase in serum alanine, methionine, and branched-chain amino acid concentrations after the test meal and a similar pattern of (15)N enrichment in serum amino acids for a given tracer. When [(15)N]Ala was included in the test meal, (15)N appeared rapidly in serum alanine and glutamine, to a minor degree in leucine and isoleucine, and not at all in other circulating amino acids. With [(15)N]SPI, there was a slow appearance of the label in all serum amino acids analyzed. Despite the different serum amino acid labeling, protein restriction reduced the postmeal transfer of dietary (15)N in [(15)N]Ala or [(15)N]SPI into [(15)N]urea by similar amounts (38 and 43%, respectively, not significant). The response of the subjects with IDDM was similar to that of the normal subjects. Information about adaptive reductions in dietary amino acid catabolism obtained by adding [(15)N]Ala to a test meal appears to be equivalent to that obtained using an intrinsically labeled protein tracer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".