Long-chain inulin increases dendritic cells in the Peyer's patches and increases<i>ex vivo</i>cytokine secretion in the spleen and mesenteric lymph nodes of growing female rats, independent of zinc status
Bibliographic record
Abstract
Prebiotics may increase Zn absorption, a mineral known to play a central role in the immune system. Zn-deficient states are characterised by suppressed immune function, while prebiotics may improve both gut and cell-mediated immunity. Our objective was to determine if inulin alters the number and proportion of immune cells in the spleen, mesenteric lymph nodes (MLN) and Peyer's patches (PP), ex vivo cytokine secretion, intestinal permeability and Zn status in healthy as well as Zn-deficient rats. Weanling female rats were fed diets supplemented with 5 % cellulose (CEL) or 5 % inulin (PRE) for 4 weeks. The rats received the CEL or PRE diet ad libitum (ZN) or in restricted amounts (DR), or deficient in Zn (ZD) for another 4 weeks. The PRE-fed rats had a higher number and proportion of dendritic cells in PP, and greater ex vivo secretion of IL-2, IL-10 and interferon-gamma from spleen and MLN cells compared with CEL-fed rats. PRE reduced the number and proportion of T cell receptor (TCR)-alphabeta+CD8+ cells in spleen and CD45RA+ cells in MLN compared with CEL. ZD rats had lower serum IgG2a and T cell numbers in MLN compared with ZN and DR rats. TCRgammadelta+ cell numbers in PP were higher in ZD-PRE rats compared with ZD-CEL rats. Femur Zn concentrations of DR-PRE rats were higher than those of DR-CEL rats. Intestinal permeability was unchanged. The higher proportion and number of dendritic cells in the PP of inulin-fed rats indicates a need for further research on how prebiotics and their metabolites affect immune function possibly through intestinal dendritic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".