High dosage of zinc modulates T-cells in a time-dependent manner within porcine gut-associated lymphatic tissue
Bibliographic record
Abstract
Zn serves as a powerful feed additive to reduce post-weaning diarrhoea in pigs. However, the mechanisms responsible for Zn-associated effects on the adaptive immune responses following feeding of a very high dosage of Zn remain elusive. In this study, we examined the T-cell response in gut-associated lymphatic tissues of seventy-two weaned piglets. Piglets received diets with 57 mg Zn/kg (low Zn concentration, LZn), 164 mg Zn/kg (medium Zn concentration, MZn) or 2425 mg Zn/kg (high Zn concentration, HZn) mg Zn/kg feed for 1, 2 or 4 weeks. We observed that feeding the HZn diet for 1 week increased the level of activated T-helper cells (CD4+ and CD8α dim) compared with feeding MZn and LZn (P<0·05). In addition, we observed higher transcript amounts of interferon γ and T-box 21 (TBET) in the HZn group compared with the MZn and LZn groups (P<0·05). A gene set enrichment analysis revealed an over-representation of genes associated with 'cytokine signalling in immune system'. Remarkably, feeding of a very high Zn dosage led to a switch in the immune response after 2 weeks. We detected higher relative cell counts of CD4+CD25high regulatory T-helper cells (P<0·05) and a higher expression of forkhead box P3 (FOXP3) transcripts (P<0·05). After 4 weeks of feeding a high-dosage Zn diet, the relative CD4+ T-cell count (P<0·05) and the relative CD8β + T-cell count (P<0·1) were reduced compared with the MZn group. We hypothesise that after 1 week the cellular T-helper 1 response is switched on and after 2 weeks it is switched off, leading to decreased numbers of T-cells.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".