Regional Differences in Susceptibiity of Bronchial Epithelium to Mesenchymal Transition and Inhibition by the Macrolide Antibiotic Azithromycin
Bibliographic record
Abstract
OBJECTIVE: Dysregulated repair following epithelial injury is a key forerunner of disease in many organs, and the acquisition of a mesenchymal phenotype by the injured epithelial cells (epithelial to mesenchymal transition, EMT) may serve as a source of fibrosis. The macrolide antibiotic azithromycin and the DNA synthesis inhibitor mycophenolate are in clinical use but their mechanism of action remains unknown in post-transplant bronchiolitis obliterans syndrome (BOS). Here we determined if regional variation in the EMT response to TGFβ1 underlies the bronchiolocentric fibrosis leading to BOS and whether EMT could be inhibited by azithromycin or mycophenolate. METHODS/RESULTS: We found that small and large airway epithelial cells from stable lung transplant patients underwent EMT when stimulated with TGFβ1, however mesenchymal protein expression was higher and loss of epithelial protein expression more complete in small airway epithelial cells. This regional difference was not mediated by changes in expression of the TGFβRII or Smad3 activation. Azithromycin potentially inhibited EMT in both small and large airway epithelial cells by inhibiting Smad3 expression, but not activation. CONCLUSION: Collectively, these observations provide a biologic basis for a previously unexplained but widely observed clinical phenomena, and a platform for the development of new approaches to fibrotic diseases.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".