An Assessment of Epithelial and Mesenchymal Phenotypes in Experimental and Clinical Pulmonary Fibrosis
Bibliographic record
Abstract
Epithelial injury has been implicated as a driving factor for the pathogenesis of idiopathic pulmonary fibrosis (IPF). In this study we investigated changes in epithelial and mesenchymal markers in experimental models of fibrosis and associated this with IPF. TGFβ1induced an epithelial to mesenchymal transition (EMT) phenotype in A549 cells and normal human bronchial epithelial cells, with A549 cells exhibiting a more profound transition to a mesenchymal phenotype. TGFβ1overexpression in the lungs of mice resulted in an early increase in mesenchymal cell markers and apoptotic genes that preceded collagen deposition, suggesting an early epithelial injury triggers the downstream fibrotic response. In contrast, bleomycin had a gradual increase in mesenchymal cell marker and a decrease in E-cadherin expression that correlated with collagen protein deposition. Finally, we compared normal healthy lung tissue with surgical lung biopsies from IPF patients and observed alterations in epithelial and mesenchymal cell markers, as well as an increase in the apoptotic marker GSK3β. Interestingly, the mesenchymal changes were more profound in rapidly progressive patients in comparison to IPF patients with slowing progressing disease. In summary, this study provides evidence of alterations in epithelial and mesenchymal markers in experimental models of lung fibrosis and how these findings are relevant to clinical disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".