Airway epithelial repair in health and disease: Orchestrator or simply a player?
Bibliographic record
Abstract
Epithelial cells represent the most important surface of contact in the body and form the first line of defence of the body to external environment. Consequently, epithelia have numerous roles in order to maintain a homeostatic defence barrier. Although the epithelium has been extensively studied over several decades, it remains the focus of new research, indicating a lack of understanding that continues to exist around these cells in specific disease settings. Importantly, evidence is emerging that airway epithelial cells in particular have varied complex functions rather than simple passive roles. One area of current interest is its role following injury. In particular, the epithelial-specific cellular mechanisms regulating their migration during wound repair remain poorly understood and remain an area that requires much needed investigation. A better understanding of the physiological, cellular and molecular wound repair mechanisms could assist in elucidating pathological processes that contribute to airway epithelial pathology. This review attempts to highlight migration-specific and cell-extracellular matrix (ECM) aspects of repair used by epithelial cells under normal and disease settings, in the context of human airways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".