Interleukin-33 in asthma: insights into pro-inflammatory roles of airway structural cells
Bibliographic record
Abstract
Interleukin-33 (IL-33) is a novel cytokine that triggers inflammatory immune responses, but evidence of its role in human asthma, a common allergic airway disease, is lacking. There is also a paucity of information regarding which cells express IL-33, and what conditions promote its expression. We sought to investigate whether IL-33 is expressed in the lung tissue from patients with asthma. We obtained lung biopsy tissue specimens from asthmatic adults and from healthy control subjects, along with normal primary cells from human airways that were cultured in vitro . We studied expression of IL-33 in lung tissue specimens, and determined whether conditions seen in asthma promote IL-33 expression in vitro . We also assessed whether IL-33 expression is sensitive to glucocorticoid treatment. Higher expression of IL-33 is detected in lung tissue from asthmatic patients compared to control subjects. IL-33 expression correlates TNF-α e.g. a hallmark of inflammation. Airway epithelium, smooth muscle cells and endothelium are all sources of IL-33. When exposed to inflammatory conditions, in vitro cultured bronchial smooth muscle and epithelial cells increased their IL-33 expression, which surprisingly remained intracellular. Finally, glucocorticoid did not significantly reduce TNF-α-induced IL-33 expression. Our study first describes IL-33 expression in asthma; it is increased in the lungs from asthmatics, and is enhanced under asthma-like in vitro conditions. IL-33 originates from structural cells of the airways and its expression does not respond to classic anti-inflammatory drug, thus reinforcing its relevance as a potential therapeutic target to treat asthma.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".