IL-27 and asthma endotypes- Is there a connection
Bibliographic record
Abstract
Background: Interleukin-27 (IL-27) may contribute to steroid-resistant asthma, however, the mechanisms mediating these effects and association of IL-27 with asthma endotypes are unclear. AIM: The study aims to examine sputum IL-27 mRNA and protein levels in asthma endotypes and inflammation. Methods: Induced sputum was collected from 80 adults with stable asthma; 30 neutrophilic asthma (NA), 30 eosinophilic asthma (EA) and 20 paucigranulocytic asthma (PGA). Total and differential cell counts, mRNA and protein expression of IL-27 and TNF-α were assessed. Results: Patients had a mean age of 55 years and 59% were female. Gene expression for IL-27EBI3 and TNF-α were increased in NA compared with EA and PGA (p<0.01) while IL-27p28 was reduced in EA compared with NA and PGA (p<0.01). Both IL-27 subunits were negatively associated with eosinophil proportion (%, p28 r=-0.31; EBI3 r=-0.26; p<0.05) but only EBI3 was associated with neutrophil % (r=0.47, p<0.01). IL-27 protein levels differed significantly and were highest in EA compared with PGA (p=0.02) and significantly associated with eosinophil % (r=0.34, p=0.01). TNF-α protein level was significantly increased in NA compared with PGA (p=0.03) and associated positively with total cells (r=0.61, p<0.01) and neutrophil number (r=0.56, p<0.01) and negatively with FEV1% predicted (r=-0.28, p=0.04). No significant correlation existed between mRNA and protein levels for IL-27 or TNF-α (p>0.05). Conclusion: Lack of any correlation between IL-27R, IL-27p28 and IL-27EBI3 mRNA with IL-27 protein suggests differential translation of IL-27 in asthma endotypes. Association between eosinophil% and IL-27 protein suggests a functional role of IL-27 in EA.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".