Increased Expression of IL-33 in Severe Asthma: Evidence of Expression by Airway Smooth Muscle Cells
Bibliographic record
Abstract
IL-33, a new member of the IL-1 cytokine family, promotes Th2 inflammation, but evidence on the implications of this cytokine in asthma is lacking. IL-33 would be mainly expressed by structural cells, but whether proinflammatory cytokines modulate its expression in airway smooth muscle cells (ASMC) is unknown. Endobronchial biopsies were obtained from adults with mild (n = 8), moderate (n = 8), severe (n = 9), asthma and from control subjects (n = 5). Immunocytochemistry, laser-capture microdissection, reverse transcriptase, and real-time quantitative PCR were used for determining IL-33 expression in the lung tissues. ASMC isolated from resected lung specimens were cultured with proinflammatory cytokines and with dexamethasone. IL-33 expression by ASMC was determined by PCR, ELISA, and Western blotting. Higher levels of IL-33 transcripts are detected in biopsies from asthmatic compared with control subjects, and especially in subjects with severe asthma. ASMC show IL-33 expression at both protein and mRNA levels. IL-33 and TNF-alpha transcript levels correlate in the lung tissues, and TNF-alpha up-regulates IL-33 expression by cultured ASMC in a time- and dose-dependent manner. IFN-gamma also increases IL-33 expression and shows synergistic effect with TNF-alpha. Dexamethasone fails to abolish TNF-alpha-induced IL-33 up-regulation. IL-33 expression increases in bronchial biopsies from subjects with asthma compared with controls, as well as subjects with asthma severity. ASMC are a source of the IL-33 cytokine. Our data propose IL-33 as a novel inflammatory marker of severe and refractory asthma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".