Lung epithelial C/EBPβ is necessary for the integrity of inflammatory responses to cigarette smoke
Bibliographic record
Abstract
CCAAT/enhancer-binding protein-β (C/EBPβ) is a key intracellular regulator of inflammatory signalling; however, its role in pulmonary inflammation is unknown. Therefore, we characterised the role of C/EBPβ in the airway epithelial response to cigarette smoke. mRNA expression in airway epithelium of current, former, and never-smokers, as well as in vitro cigarette smoke extract-treated human airway epithelial cells, were analysed by microarray and real-time PCR, respectively. Mice with lung epithelial-specific inactivation of C/EBPβ (CebpbΔLE) were exposed to cigarette smoke for 4 or 11 days. Lung histology, bronchoalveolar lavage cell differentials, and pulmonary expression of inflammatory and innate immune mediators were assessed. C/EBPβ expression was down-regulated in the airway epithelium of both current and former smokers compared to never-smokers, as well as in cigarette smoke extract-treated human airway epithelial cells. Cigarette smoke-exposed CebpbΔLE mice displayed blunted neutrophil influx, and compromised induction of neutrophil chemoattractants GROα and MIP-1γ, inflammatory cytokines TNFα and IL-1β, and the innate immunity gene SAA3, compared to smoke-exposed controls. Inhibition of C/EBPβ in human airway cells in vitro caused similarly compromised response to cigarette smoke extract. In summary, this suggests a previously unknown role for C/EBPβ and the airway epithelium in mediating inflammatory and innate immune responses to cigarette smoke.
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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 | 0.000 |
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".