The role of epithelial injury and repair in the origins of asthma
Bibliographic record
Abstract
PURPOSE OF REVIEW: We currently understand little about the mechanisms that lead to asthma. The bronchial epithelium is the first cell layer of contact with the environment and as such is an especially attractive target in which to identify novel mechanisms and new therapeutic strategies in disease development. We discuss the role of epithelial injury and wound repair in the origins of asthma. RECENT FINDINGS: The presence of inflammation, thickening of the basement membrane and angiogenesis have been described in bronchial biopsies from asthmatic children. We and others have demonstrated the utility of bronchial brushings from children for the isolation, characterization and culture of primary epithelial cells. The results of these experiments suggest that intrinsic differences exist between asthmatic and nonasthmatic epithelial cells. SUMMARY: It is becoming increasingly clear from studies involving adults and, more recently, children, that the epithelium orchestrates inflammatory and remodeling responses of the airway. Equally clear is that the asthmatic epithelium responds inappropriately to challenge and displays signs of dysregulated repair. Understanding the regulatory mechanisms involved in these processes, including the role of resident/recruited progenitor cells, is crucial if we are to halt the progression of asthma when the disease first manifests in childhood.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".