Available versus digestible amino acids – new stable isotope methods
Bibliographic record
Abstract
The nutritive value of food protein sources is dependent on the amino acid composition and the bioavailability of the nutritionally indispensable amino acids. Traditionally the methods developed to determine amino acid bioavailability have focused on intestinal absorption or digestibility, which is calculated as the percent of amino acid intake that does not appear in digesta or faeces. Traditional digestibility based methods do not always account for gut endogenous amino acid losses or absorbed amino acids which are unavailable due to the effect of heat processing and the presence of anti-nutritional factors, though methods have been developed to address these issues. Furthermore, digestibility based methods require the use of animal models, thus there is a need to develop in vivo methods that can be applied directly in human subjects to identify the proportion of dietary amino acids which is bioavailable, or metabolically available to the body for protein synthesis following digestion and absorption. The indicator amino acid oxidation (IAAO) method developed in our laboratory for humans has been systematically applied to determine almost all indispensable amino acid requirements in adult humans. Oxidation of the indicator amino acid is inversely proportional to whole body protein synthesis and responds rapidly to changes in the bioavailability of amino acids for metabolic processes. Using the IAAO concept, we developed a new in vivo method in growing pigs, pregnant sows and adult humans to identify the metabolic availability of amino acids in foods. The stable isotope based metabolic availability method is suitable for rapid and routine analysis in humans, and can be used to integrate amino acid requirement data with dietary amino acid availability of foods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".