The airway epithelium: more than just a structural barrier
Bibliographic record
Abstract
The mammalian airway is lined by a variety of specialized epithelial cells that not only serve as a physical barrier but also respond to environment-induced damage through the release of biologically active factors and constant cellular renewal. The lung epithelium responds to environmental insults such as pathogens, cigarette smoke and pollution by secreting inflammatory mediators and antimicrobial peptides, and by recruiting immune cells to the site of infection or damage. When the epithelium is severely damaged, basal cells and Clara cells that have stem-cell-like properties are capable of self-renewal and proliferation in the affected area, to repair the damage. In order to effectively fight off infections, the epithelium requires the assistance of neutrophils recruited from the peripheral circulation through transendothelial followed by transepithelial migration events. Activated neutrophils migrate across the epithelium through a series of ligand-receptor interactions to the site of injury, where they secrete proteolytic enzymes and oxidative radicals for pathogen destruction. However, chronic activation and recruitment of neutrophils in airway diseases such as chronic obstructive pulmonary disease and asthma has been associated with tissue damage and disease severity. In this paper, we review the current understanding of the airway epithelial response to injury and its interaction with inflammatory cells, in particular the neutrophil.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".