Increased levels of serum IL-17 and induced sputum neutrophil percentage are associated with severe early-onset asthma in adults
Bibliographic record
Abstract
BACKGROUND: Differences between adult patients with severe early-onset and late-onset asthma have not been well studied. OBJECTIVES: To determine the phenotypic distinction regarding age at onset in patients with severe asthma. METHODS: The present study enrolled thirty-two patients with severe early-onset (onset age < 12 years) asthma and thirty-two patients with severe late-onset (onset age > 12 years) asthma. Severe asthma was defined according to Global Initiative for Asthma criteria. The clinical, spirometric, and laboratory parameters were collected for group comparisons. RESULTS: Among the 64 patients included (mean age, 46.22 ± 13.90 years; 53.1% male), the mean percent of predicted forced expiratory volume in 1 s (FEV1) was 68.43 ± 20.55%. Patients with severe early-onset asthma had a younger age, longer duration of asthma, higher rate of family history, and better small-airway function (MEF25% and MMEF75/25%) compared with severe late-onset asthma. Furthermore, levels of serum IL-17 and sputum neutrophil percentage were significantly higher for patients with severe early-onset asthma (P = 0.016, 0.033, respectively). Multiple logistic regression analysis revealed that increased serum IL-17 (odds ratio = 1.065, P = 0.016) was independently associated with severe early-onset asthma. The combination of serum IL-17 and sputum neutrophil percentage yielded a sensitivity of 80.0% and a specificity of 86.7% for identifying patients with severe early-onset asthma. CONCLUSIONS: Patients with severe early-onset asthma exhibit elevated levels of serum IL-17 and sputum neutrophil percentage, suggesting a potential role in the pathogenesis of severe early-onset phenotype.
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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 | 0.001 |
| 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 | 0.000 | 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".