Dietary Zinc Deficiency in Rodents: Effects on T-Cell Development, Maturation and Phenotypes
Bibliographic record
Abstract
Zinc deficiency is one of the leading risk factors for developing disease and yet we do not have a clear understanding of the mechanisms behind the increased susceptibility to infection. This review will examine the interrelationships among the hypothalamus-pituitary-adrenal stress axis, p56(lck), and T-cell maturation in both zinc deficiency and responses during zinc repletion. We will highlight differences between the adult mouse model (wasting malnutrition) and growing rat model (stunting malnutrition) of dietary zinc deficiency and discuss the use of various controls to separate out the effects of zinc deficiency from the associated malnutrition. Elevated serum corticosterone in both zinc deficient and pair-fed rats does not support the hypothesis that zinc deficiency per se leads to corticosterone-induced apoptosis and lymphopenia. In fact, the zinc deficient rat does not have lymphopenia. Thymocytes from zinc deficient mice and rats have elevated levels of p56(lck), a signalling protein with a zinc clasp structure, but this does not appear to affect thymocyte maturation. However, post-thymic T-cell maturation appears to be altered based on the lower proportion of splenic late thymic emigrants in zinc deficient rats. Fewer new T-cells in the periphery could adversely affect the T-cell repertoire and contribute to immunodeficiency in zinc deficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".