The Epithelial Cell in Lung Health and Emphysema Pathogenesis
Bibliographic record
Abstract
Cigarette smoking is the primary cause of the irreversible lung disease emphysema. Historically, inflammatory cells such as macrophages and neutrophils have been studied for their role in emphysema pathology. However, recent studies indicate that the lung epithelium is an active participant in emphysema pathogenesis and plays a critical role in the lung's response to cigarette smoke. Tobacco smoke increases protease production and alters cytokine expression in isolated epithelial cells, suggesting that these cells respond potently even in the absence of a complete inflammatory program. Tobacco smoke also acts as an immunosuppressant, reducing the defense function of airway epithelial cells and enhancing colonization of the lower airways. Thus, the paradigm that emphysema is strictly an inflammatory-cell based disease is shifting to consider the involvement of resident epithelial cells. Here we review the role of epithelial cells in lung development and emphysema. To better understand tobacco-epithelial interactions we performed microarray analyses of RNA from human airway epithelial cells exposed to smoke extract for 24 hours. These studies identified differential regulation of 425 genes involved in diverse biological processes, such as apoptosis, immune function, cell cycle, signal transduction, proliferation, and antioxidants. Some of these genes, including VEGF, glutathione peroxidase, IL-13 receptor, and cytochrome P450, have been previously reported to be altered in the lungs of smokers. Others, such as pirin, cathepsin L, STAT1, and BMP2, are shown here for the first time to have a potential role in smoke-associated injury. These data broaden our understanding of the importance of epithelial cells in lung health and cigarette smoke-induced emphysema.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".