Neutrophil‐epithelial contact disrupts epithelial barrier function and is dependent on protease‐activated receptors (PAR)‐1 and ‐4
Bibliographic record
Abstract
Neutrophil (PMN) infiltration through mucosal surfaces is a hallmark of inflammatory diseases and contributes to epithelial injury. Past research has shown that PMN contact increases epithelial permeability and induces epithelial signaling events prior to transepithelial migration. In this study we sought to better characterize the mechanisms that influence the disruption of epithelial barrier function induced by PMN contact. In the presence of a transepithelial fMLP gradient, PMN contact with basolateral surfaces of intestinal epithelial (T84 & SK‐CO15) monolayers resulted in decreased transepithelial electrical resistance (TER) and correlated with diminished levels of tight junction proteins occludin and ZO‐1, but not E‐cadherin. Pre‐incubation with 1mM AEBSF (serine protease inhibitor) prevented the decrease in TER induced by PMN contact. Conversely, incubation with 10μg/ml PMN elastase and proteinase‐3, but not cathepsin G, decreased TER. Treatment of epithelial monolayers with 50μM PAR agonists TFLLR (PAR‐1)/SLIGRL (PAR‐2)/AYPGKF (PAR‐4), or 5μM antagonists SCH79797 (PAR‐1)/P4pal‐10 (PAR‐4) also prevented the decrease in TER induced by PMN contact. Collectively, these findings suggest that activation of PAR signaling events by PMN proteases may enhance epithelial permeability and facilitate passage of migrating PMN through the paracellular space. (CAG/CIHR/Axcan Pharma Inc/NIH)
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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) | 0.004 | 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".