Fibroblast Signal Transducer and Activator of Transcription 4 Drives Cigarette Smoke–Induced Airway Fibrosis
Bibliographic record
Abstract
Cigarette smoke-induced emphysema and small airway remodeling are the anatomic bases of chronic obstructive pulmonary disease (COPD), but the pathogenesis of these changes is unclear, and current treatments for COPD are minimally effective. To evaluate the role of signal transducer and activator of transcription (STAT)-4 in cigarette smoke-induced small airway remodeling, we used C57BL/6J (wild type [WT]) and STAT4-/- mice exposed to air or cigarette smoke for 6 months and isolated airway and parenchymal fibroblasts. We also compared the results with those obtained with human fibroblasts. We found that STAT4-/- mice were protected against smoke-induced small airway remodeling but not emphysema. STAT4 is abundantly expressed in airway compared with parenchymal-derived fibroblasts isolated from normal human and murine lung. WT airway fibroblasts proliferate faster than STAT4-/- airway fibroblasts, whereas there is no difference between strains for parenchymal fibroblasts. IL-12 is up-regulated in the lung after smoke exposure, and IL-12 receptor B2 is expressed on airway and parenchymal fibroblasts in mouse and human lung. Treatment with IL-12 causes phosphorylation of STAT4 in WT airway fibroblasts. Exposure of WT airway, but not parenchymal, fibroblasts to IL-12 causes increased expression of collagen 1α1 and transforming growth factor β1, factors involved in small airway remodeling, whereas STAT4-/- fibroblasts are unresponsive to IL-12. These results indicate that IL-12 can drive small airway remodeling via STAT4 signaling and suggest that treatment with clinically available anti-IL-12p40 drugs might provide a new approach to preventing small airway remodeling in cigarette smokers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".