Nutrients and their role in host resistance to infection
Bibliographic record
Abstract
Almost all nutrients in the diet play a crucial role in maintaining an "optimal" immune response, such that deficient and excessive intakes can have negative consequences on immune status and susceptibility to a variety of pathogens. Iron and vitamin A deficiencies and protein-energy malnutrition are highly prevalent worldwide and are important to the public health in terms of immunocompetence. There are also nutrients (i.e., glutamine, arginine, fatty acids, vitamin E) that provide additional benefits to immunocompromised persons or patients who suffer from various infections. The remarkable advances in immunology of recent decades have provided insights into the mechanisms responsible for the effects of various nutrients in the diet on specific functions in immune cells. In this review, we will present evidence and proposed mechanisms for the importance of a small group of nutrients that have been demonstrated to affect host resistance to infection will be presented. An inadequate status of some of these nutrients occurs in many populations in the world (i.e., vitamin A, iron, and zinc) where infectious disease is a major health concern. We will also review nutrients that may specifically modulate host defense to infectious pathogens (long-chain polyunsaturated n-3 fatty acids, vitamin E, vitamin C, selenium, and nucleotides). A detailed review of the effect of long-chain polyunsaturated n-3 fatty acids on host defense is provided as an example of how the disciplines of nutrition and immunology have been combined to identify key mechanisms and propose nutrient-directed management of immune-related syndromes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".