Regulation of IL-33 by Oncostatin M in Mouse Lung Epithelial Cells
Bibliographic record
Abstract
IL-33 modulates both innate and adaptive immune responses at tissue sites including lung and may play critical roles in inflammatory lung disease. Although IL-33 expression can be altered upon NF-Kappa B activation, here we examine regulation by Oncostatin M, a gp130 cytokine family member, in mouse lung tissue. Responses were assessed in BALB/c mouse lung at day 7 of transient overexpression using endotracheally administered adenovirus encoding OSM (AdOSM) or empty vector (AdDel70). Whole lung extracts showed induction of IL-33 mRNA (>20-fold) and protein (10-fold increase in immunoblots) by AdOSM relative to AdDel70. Immunohistochemistry for IL-33 indicated a marked induction of nuclear staining in alveolar epithelial cellsin vivo. Oncostatin M stimulated IL-33 mRNA and IL-33 full length protein in C10 mouse type 2 alveolar epithelial cells in culture in time-dependent and dose-dependent fashion, whereas IL-6, LIF, IL-31, IL-4, or IL-13 did not, and TGFβrepressed IL-33. IL-33 induction was associated with activation of STAT3, and pharmacological inhibition of STAT3 ameliorated IL-33 levels. These results indicate Oncostatin M as a potent inducer of IL-33 in mouse lung epithelial cells and suggest that an OSM/IL-33 axis may participate in innate immunity and inflammatory conditions in lung.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".