Epithelial cell dysfunction, a major driver of asthma development
Bibliographic record
Abstract
Airway epithelial barrier dysfunction is frequently observed in asthma and may have important implications. The physical barrier function of the airway epithelium is tightly interwoven with its immunomodulatory actions, while abnormal epithelial repair responses may contribute to remodelling of the airway wall. We propose that abnormalities in the airway epithelial barrier play a crucial role in the sensitization to allergens and pathogenesis of asthma. Many of the identified susceptibility genes for asthma are expressed in the airway epithelium, supporting the notion that events at the airway epithelial surface are critical for the development of the disease. However, the exact mechanisms by which the expression of epithelial susceptibility genes translates into a functionally altered response to environmental risk factors of asthma are still unknown. Interactions between genetic factors and epigenetic regulatory mechanisms may be crucial for asthma susceptibility. Understanding these mechanisms may lead to identification of novel targets for asthma intervention by targeting the airway epithelium. Moreover, exciting new insights have come from recent studies using single-cell RNA sequencing (scRNA-Seq) to study the airway epithelium in asthma. This review focuses on the role of airway epithelial barrier function in the susceptibility to develop asthma and novel insights in the modulation of epithelial cell dysfunction in asthma.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".