Interleukin-9 enhances interleukin-5 receptor expression, differentiation, and survival of human eosinophils
Bibliographic record
Abstract
Abstract Interleukin-9 (IL-9) has been implicated in the pathogenesis of allergic disorders. To examine the interaction between IL-9 and eosinophils, we evaluated mature peripheral blood eosinophils for their expression of the specific α-subunit of the IL-9 receptor (IL-9R–α). The expression of IL-9R–α by human eosinophils was detected at the messenger RNA (mRNA) and protein levels by reverse transcriptase–polymerase chain reaction (RT-PCR), flow cytometry, and immunocytochemical analysis, respectively. Functional analyses demonstrated that recombinant human (rh)IL-9 inhibited in vitro peripheral blood human eosinophil apoptosis in a concentration-dependent manner. We then examined the role of IL-9 in eosinophil differentiation using the human cord blood CD34+cells and human promyelocytic leukemia cells (HL-60). The addition of IL-9 to CD34+ cells cultured in IL-3 and IL-5 enhanced eosinophil development, and IL-9 alone induced the expression of IL-5R–α. IL-9 also up-regulated the IL-5R–α chain cell surface expression during terminal eosinophil differentiation of the HL-60 cell line. Our findings suggest that IL-9 may potentiate in vivo eosinophil function by increasing their survival and IL-5–mediated differentiation and maturation. Taken together, these results suggest a mechanism by which IL-9 potentiates airway and tissue eosinophilia.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".