Diabetes-prone BioBreeding rats do not have a normal immune response when weaned to a diet containing fermentable fibre
Bibliographic record
Abstract
Diet is known to modulate the development of diabetes in diabetes-prone BioBreeding (BBdp) rats. The objective of the present study was to determine the effect of fermentable fibre (FF) on immune function in BBdp and diabetes-resistant BioBreeding (BBdr) rats after weaning. Weanling BBdp (thirty-six to thirty-eight per diet) and BBdr rats (thirty to thirty-two per diet) were fed a nutritionally complete, semi-purified, casein-based diet containing either cellulose (control diet, 8 % w/w) or FF (3.2 % cellulose+4.8 % w/w inulin). At 35 d, the small intestine was excised and lymphocytes isolated from spleen, mesenteric lymph nodes and Peyer's patches. Feeding FF to both BBdr and BBdp rats affected the production of anti-inflammatory cytokines (P=0.02). In BBdr rats, feeding FF compared with cellulose resulted in an increased small intestinal length (P=0.0031), higher proliferative (stimulation) index from both splenocytes (P=0.001) and mesenteric lymph nodes (P=0.04), and an increased proportion of CD8+ T-cells in the Peyer's patches (P=0.003). We did not observe an effect of diet on the number of IgA-bearing cells in the jejunum from BBdr rats. Feeding FF to BBdp rats did not affect the same parameters. BBdp rats had both a higher proportion of B-cells in the Peyer's patches (P=0.01) and a higher number of IgA+ cells in the jejunum (P=0.0036) when fed a diet containing FF, a response not observed in BBdr rats. We demonstrate that several aspects of the BBdp immune system respond differently than that of BBdr rats when challenged at weaning with FF.
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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 | 0.000 |
| 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) | 0.003 | 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".