Late Breaking Abstract - Cytokine micro-environment in moderate-to-severe asthma with airway autoantibodies
Bibliographic record
Abstract
Introduction: Irrespective of anti-inflammatory therapies, ~55% of moderate-to-severe asthma patients have sputum IgG autoantibodies (aAbs, against eosinophilic degranulation products including nuclear/extra nuclear antigens), associated with asthma severity. Aim: To investigate the associated cytokine microenvironment in patients with sputum autoantibodies. Methods: Cytokines (T2: IL-5, IL-4, IL-13, IL-33; T1/17: IL-12, IL-15, IL-17A, IFNγ; inflammasomes: IL-1β, IL-18; B-cell activity: IL-6, TNFα, BAFF; EllaTM multiplex, Biotechne) (PMID: 38197516) and aAbs (PMID: 35777765) were evaluated in 223 prospectively collected sputum supernatants from 144 moderate-to-severe asthma patients. A composite airway autoimmune score (CAAS) detectable sputum aAbs is considered CAAS+. Results: 112/223 samples (50%) (n=75/144, 52% of patients) were CAAS+ with anti-histone (39%) and anti-nucleosome (36%) being most prevalent. Samples were primarily eosinophilic (~50%), followed by paucigranulocytic (~30%) and mixed granulocytic (~20%). The most prevalent cytokines in the CAAS+ group were IL-13, IL-33 (T2), IL-18/IL-1β (inflammasomes), TNFα and BAFF (B cell activity) (p<0.05). CAAS+ patients had higher ACQ-5 scores despite current inflammatory treatments vs. CAAS- (2.0±1.2 vs 0.24±0.4, p<0.0001). When stratified by ongoing biologic therapies, 39% of patients were CAAS+ with elevated IL-5 [mean, pg/mL: 13.8±46 vs 0.3±0.62, p<0.0001], IL-13 [ 5.6±6 vs 1.8±2, p=0.005], and IL-33 [ 5.7±4 vs 2.9±2, p=0.006], irrespective of the type of biologic. Conclusions: Patients with airway autoantibodies have a cytokine signature indicative of ongoing T2, inflammasome, and B cell activity irrespective of treatment and response.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".