Model to assess muscle protein turnover: domain of validity using amino acyl-tRNA vs. surrogate measures of precursor pool
Bibliographic record
Abstract
Current models to measure protein turnover across muscle bed are based on many surrogate measures of amino acyl-tRNA. We measured muscle protein turnover based on tracer-to-tracee ratios of the stable isotopes of leucine, phenylalanine, and ketoisocaproate (KIC) in artery and vein and muscle amino acyl-tRNA and muscle tissue fluid (TF) in 26 healthy subjects. A three-compartment model calculation based on arteriovenous and tRNA measurements was first performed and its domain of validity assessed. The results were then compared with those using simpler approaches based on surrogate measures of tRNA such as those of TF and KIC and a one-compartment model based on arteriovenous amino acids. In 96% of cases, the model using tRNA was applicable, but only in a lower percentage of cases were the results using surrogate measures applicable. Protein breakdown, protein synthesis, and shunting of amino acids from artery to vein were consistently underestimated, and fluxes of amino acid from artery to intracellular compartment and from intracellular compartment to vein were overestimated, when surrogate measures were used. The one-compartment model also underestimated protein breakdown and synthesis. Measurements using tissue fluid gave results closer to those based on tRNA. In conclusion, a three-compartment model using arteriovenous samples and amino acyl-tRNA provides measurements of muscle protein turnover of acceptable precision in 96% of cases. The precision was unacceptable in a substantial percentage of cases, and the accuracy of the estimation of protein fluxes was significantly affected when surrogate measures were used.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".