Monocyte interaction accelerates HCl-induced lung epithelial remodeling
Bibliographic record
Abstract
BACKGROUND: Acute respiratory distress syndrome (ARDS) is characterized by overwhelming inflammatory responses and lung remodeling. We hypothesized that leukocyte infiltration during the inflammatory response modulates epithelial remodeling through a mechanism of epithelial-mesenchymal transition (EMT). METHODS: Human lung epithelial cells were treated for 30 min with hydrochloric acid (HCl). Human monocytes were then cocultured with the epithelial cells for up to 48 h, in the presence or absence of blocking peptides against lymphocyte function-associated antigen-1 (LFA-1), or tyrphostin A9, a specific inhibitor for platelet-derived growth factor (PDGF) receptor tyrosine kinase. RESULTS: Exposure of lung epithelial cells to HCl resulted in increased expression of intercellular adhesion molecule-1 (ICAM-1) and production of interleukin (IL)-8 at 24 h. The expression of the epithelial markers E-cadherin decreased while the mesenchymal markers vimentin and α-smooth muscle actin (α-SMA) increased at 24 h and remained high at 48 h. The addition of monocytes augmented the profiles of lower expression of epithelial markers and higher mesenchymal markers accompanied by increased collagen deposition. This EMT profile was associated with an enhanced production of IL-8 and PDGF. Treatment of the lung epithelial cells with the LAF-1 blocking peptides CD11a237-246 or/and CD18112-122 suppressed monocyte adhesion, production of IL-8, PDGF and hydroxyproline as well as EMT markers. Treatment with tyrphostin A9 prevented the EMT profile shift induced by HCl stimulation. CONCLUSIONS: The interaction between epithelial cells and monocytes enhanced epithelial remodelling after initial injury through EMT signalling that is associated with the release of soluble mediators, including IL-8 and PDGF.
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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) | 0.000 | 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 | 0.001 |
| 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".