Glutamine and arginine: immunonutrients for improved health
Bibliographic record
Abstract
There is considerable literature demonstrating that specific nutrients can influence immune function in health and disease. This review will examine the literature and the rational for classifying two amino acids, glutamine (gln) and arginine (arg), as "immunonutrients" during infections. An understanding of immune defenses during infections (virus, parasite, bacteria, protozoa) and metabolism of gln and arg by immune cells is necessary to understand how these nutrients can influence specific functions of the immune system. This review focuses on several key clinical studies in immunosuppressed individuals (burn patients, individuals with cancer and HIV infection, and those undergoing surgery or who have experienced major traumas) that have tested the hypothesis that the provision of gln and/or arg is beneficial to immune function and clinical outcome. These clinical studies support the dietary "essentiality" of these two nutrients for improving immune responses in most immunosuppressive states associated with high rates of infection. However, the role of these nutrients in modulating the immune changes that occur with exercise in healthy athletes demands additional experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".