Evidence of a local negative feedback mechanism via IL-5Rα shedding from airway eosinophils.
Bibliographic record
Abstract
Introduction: Interleukin (IL)-5 activates and prolongs survival of eosinophils via IL-5Rα. Loss of membrane bound IL-5Rα on eosinophils is reported to limit these effects, with implications for eosinophil activation in eosinophilic asthma (EA). Methods: 31 people with EA and 17 healthy controls provided simultaneous blood and induced sputum. Soluble (s)IL-5Rα and IL-5 were assayed in blood and sputum supernatants by ELISA. Ex-vivo blood and sputum eosinophils from people with EA were flow-sorted and expression of IL-5Rα (CD125), IL-3Rα (CD123) and CD62L enumerated with cytospins to assess purity. Results: IL-5 and eosinophils were increased in sputum and sIL-5Rα was reduced compared to blood for EA but not healthy controls (Figure 1A). IL-5Rα expression on sputum eosinophils in EA was reduced compared to blood (Figure 1C&D). There was also a reduction in CD62L and an increase in CD123 expression on sputum eosinophils (Figure 1B) and these changes were strongly correlated with expression of IL-5Rα (r = 0.89, p <0.0001 & r = -0.78, p <0.001, respectively). Further experiments confirmed loss of IL-5Rα on sputum eosinophils was not due to sample handling. Median eosinophil purity from blood was 100% (95%CI 100–100%), and sputum 99.0% (95%CI 98.9–99.6%). Conclusion: Expression of IL-5Rα is markedly reduced in sputum compared to blood in EA and corresponds with markers of eosinophil migration and activation. erj;66/suppl_69/PA4712/F1 F1 F1
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".