Regulation of in vivo phenylalanine hydroxylation by dietary tyrosine using enrichment in Apo‐B100
Bibliographic record
Abstract
We compared estimates of phenylalanine hydroxylation (Phe‐OH), across a range of tyrosine intakes, determined using plasma and ApoB‐100 phenylalanine and tyrosine enrichments, with a measurement of in vivo change in protein synthesis. Six, healthy, male subjects receiving 4.54 μmol.kg −1 .h −1 of phenylalanine, were randomized to receive each of the 7 test tyrosine intakes (3, 4.5, 6.0, 7.5, 9.0, 10.5 and 12 mg.kg − 1.d −1 ) for 3‐d. Studies were conducted at each level using the tracers of L‐[15N] phenylalanine and L‐[3,3‐2H2] tyrosine. Blood samples were collected at baseline and isotopic steady state. Repeated ANOVA using mixed model was performed to assess the effect of tyrosine intake on phenylalanine hydroxylation. Using ApoB‐100 as the sampling site, estimates of phenylalanine hydroxylation were lower than those from plasma phenylalanine and tyrosine. Apob‐100 enrichment followed a linear plateau pattern with a break point at a tyrosine intake of 6.8 mg.kg −1 .d −1 . We conclude that ApoB‐100 is a more suitable method than plasma for estimating intracellular enrichment of amino acids in the liver. (CIHR supported). Effect of Tyrosine intake on Phe‐OH (μmol.kg −1 .h −1 ) in plasma and ApoB‐100 protein image
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".