Epithelial Mesenchymal Transition in Respiratory Disease:Fact or Fiction
Bibliographic record
Abstract
Aberrant wound repair and fibrosis play a fundamental role in many major diseases concerning pulmonologists, including all forms of pulmonary fibrosis as well as COPD, asthma, cystic fibrosis (CF), bronchiolitis obliterans syndrome (BOS), and bronchiectasis. Accordingly, understanding normal and abnormal wound repair in the lung is a major objective of many academic groups and industry programs; one aspect of wound repair requiring urgent attention focuses on what drives the increased number of extracellular matrix (ECM)-secreting mesenchymal cells. Several different sources/pathways have been offered to account for the increased pool of (myo)fibroblasts. De-differentiation of airway smooth muscle cells and migration toward the basal lamina has been proposed,<sup>1</sup> as have pericytes<sup>2</sup> and endothelial-mesenchymal transition.<sup>3,4</sup> However, the most studied mechanism is epithelial-mesenchymal transition (EMT), in which epithelial cells lose epithelial characteristics and become more mesenchymal, gaining mobility and enhanced ability to secrete ECM. This highly dynamic process has been subcategorized according to the three main functions it is associated with: embryonic development (type I), wound healing and tissue repair (type II), and cancer (type III). In this translational review, the mechanisms, roles, and impact of EMT (particularly type II) in chronic lung diseases are discussed. We also evaluate whether current medications influence EMT and how we may affect this process in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".