Development of Oral Amino Acid Tracer Models to Study Muscle and Whole-body Anabolism in Humans
Bibliographic record
Abstract
Traditional stable isotope infusion methods to study muscle and whole-body anabolic responses to feeding and exercise can be invasive and logistically challenging and generally do not provide insight into the metabolic fate of ingested amino acids. Therefore, this thesis aimed to develop oral amino acid tracer models to comprehensively study muscle and whole-body anabolism in humans. In Chapter 2, we demonstrated that oral L-[1-13C]leucine and L-[ring-2H5]phenylalanine ingestion resulted in greater dietary amino acid incorporation after feeding and exercise as compared to feeding and fasting at rest, resulting in greater rates of myofibrillar protein synthesis. In Chapter 3, we demonstrated that a non-invasive breath test based on oral L-[1-13C]leucine ingestion can detect reduced dietary leucine oxidation and enhanced net leucine balance after feeding and exercise as compared to feeding at rest. And finally, in Chapter 4 we demonstrated that amino acid transporter expression was not influenced by acute feeding and/or resistance exercise and did not positively influence the incorporation of dietary amino acids for myofibrillar protein synthesis. Collectively, these findings represent viable methods and models to allow greater flexibility in testing anabolic sensitivity under a range of physiological conditions and within a wider variety of study populations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".