Recent advances in determining protein and amino acid requirements in humans
Bibliographic record
Abstract
During the past 25 years a significant amount of research has been conducted to determine amino acid requirements in humans. This is primarily due to advancements in the application of stable isotopes to examine amino acid requirements. The indicator amino acid oxidation (IAAO) method has emerged as a robust and minimally invasive technique to identify requirements. The IAAO method is based on the concept that when one indispensable dietary amino acid (IDAA) is deficient for protein synthesis, then the excess of all other IDAA, including the indicator amino acid, will be oxidized. With increasing intakes of the limiting amino acid, IAAO will decrease, reflecting increasing incorporation into protein. Once the requirement for the limiting amino acid is met there will be no further change in the indicator oxidation. The IAAO method has been systematically applied to determine most IDAA requirements in adults. The estimates are comparable to the values obtained using the more elaborate 24h-indicator amino acid oxidation and balance (24h-IAAO/IAAB) model. Due to its non-invasive nature the IAAO method has also been used to determine requirements for amino acids in neonates, children and in disease. The IAAO model has recently been applied to determine total protein requirements in humans. The IAAO method is rapid, reliable and has been used to determine amino acid requirements in different species, across the life cycle and in disease. The recent application of IAAO to determine protein requirements in humans is novel and has significant implications for dietary protein intake recommendations globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".