Sputum immunoglobulin E levels correlate with eosinophilic airway regardless of atopy
Bibliographic record
Abstract
Immunoglobulin E (IgE) is a key molecule that induces mast cell activation in allergic inflammation and contributes to type 2/eosinophilic inflammation in asthmatic airways. This cross-sectional study investigated the role of local IgE in asthmatic airways according to atopy, asthma control, and eosinophilic inflammation. A total of 31 adult patients with moderate-to-severe asthma were enrolled. The study subjects were classified into (1) atopic/non-atopic, (2) controlled/partly controlled/uncontrolled asthma and (3) eosinophilic/non-eosinophilic asthma. Serum/sputum IgE and serum/urine eosinophil-derived neurotoxin (EDN) were measured. Serum IgE levels were higher in atopic asthmatics than in non-atopic asthmatics, whereas no differences were noted in sputum IgE levels. Sputum IgE levels were significantly higher in uncontrolled asthmatics than in partly controlled or controlled asthmatics, and in eosinophilic asthmatics than in non-eosinophilic asthmatics, whereas no differences were noted in serum IgE levels. Significant correlations were observed between serum EDN and serum/sputum IgE levels. The production of local IgE in asthmatic airways could contribute to type 2/eosinophilic inflammation, irrespective of atopy, resulting in poor asthma control. Strategies targeting IgE may be effective in the management of non-atopic and atopic 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".