In vivo regulation of IL-13 induced γδ T cell inflammatory responses by IL-17 (55.10)
Bibliographic record
Abstract
Abstract The Th2 cytokine IL-13 regulates several aspects of asthma including airway inflammation, airway hyperresponsiveness (AHR) and mucus production. The Th17 cytokine, IL-17 is also known to participate in allergic disease since higher levels of IL-17 are associated with neutrophilia, particularly in severe or fatal asthma. To better understand the interaction of these cytokines in vivo, we intranasally administered recombinant IL-13 and/or IL-17 to anaesthetized mice. Administration of IL-13 alone led to an increase in airway inflammation and AHR. Intracellular cytokine staining of the recovered bronchoalveolar lavage cells revealed increased numbers of IL-17 producing T cells (both γδ and CD4). Administration of IL-17 alone had no effect on these experimental outcomes but did increase levels of tyrosine phosphorylated STAT3 in the lungs compared to saline controls. Treatment with both IL-13 and IL-17 led to an increase in the number of infiltrating eosinophils. While co-administration of IL-17 (with IL-13) had no effect on the number of infiltrating IL-17+ve CD4 cells, it decreased the number of IL-17+ve γδ T cells in the bronchoalveolar lavage. IL-17 also inhibited IL-13-induced increases in mRNA encoding IL-17, TGF beta and IL-6. Further studies examining the mechanism by which γδ T cells and IL-17 associated mediators are regulated by IL-13 and IL-17 are under way.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".