IL-13 regulates the in vivo generation of IL-17 producing T cells (163.15)
Bibliographic record
Abstract
Abstract Several features associated with allergic airways disease are regulated by the Th2 cytokine IL-13. IL-17 is also known to regulate aspects of allergic disease since production of IL-17 is associated with severe asthma and correlates with an increase in airway neutrophilia. The mechanism by which these IL-17 producing cells develop during airways disease is still unclear. To better understand what factors regulate the development of IL-17 producing cells we used an acute model of IL-13-induced airway inflammation in which IL-13 was intranasally administered to anaesthetized mice. As expected, administration of IL-13 induced airway eosinophilia, airway hyperresponsiveness, and mucus production. In addition, IL-13 induced airway neutrophilia as well as increases in expression of neutrophil chemokines. IL-13 also induced increased mRNA expression of IL-17 and IL-6 in the lung. Intracellular cytokine staining of bronchoalveolar lavage cells showed that IL-13 increased the number of IL-17+ve cells when compared to control. These cells did not produce IL-22 or IFN-gamma and were identified as either CD4 or γδ T cells. Animals were then treated with IL-13 in the presence of BrdU to follow development of IL-17 expresing cells in vivo. The majority of these cells incorporated BrdU suggesting that these IL-17+ve cells proliferated in the lung. Further studies to characterize these IL17+ve T cells 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".