Lung epithelial cells downmodulate neutrophil‐endothelial interactions in response to inflammatory stimuli
Bibliographic record
Abstract
Endothelial cells (EC) are in close proximity to epithelial cells (EpC) in the lung. To mimic this in vitro , we established a bilayer culture model on Transwell filters to examine the interactions between EC and lung EpC during leukocyte adhesion/transendothelial migration. Human umbilical vein EC (HUVEC) were seeded on one surface of a filter with 0.4 μm pores, and alveolar epithelial‐derived A549 cells were seeded on the other side of the same filter. Using various stimuli, including interleukin‐1 (IL‐1), tumor necrosis factor‐α (TNF‐α), and killed E. coli, human neutrophil adhesion and transendothelial migration was diminished when HUVEC were grown adjacent to lung EpC compared with when grown alone. This was observed regardless of whether the endothelium was activated apically or basolaterally. Similarly, E‐selectin upregulation on endothelium grown in bilayers was significantly less than that of endothelium grown alone with the same stimuli as above. However, these differences were more pronounced when the endothelium was activated basolaterally, suggesting that the epithelium mediates its protective effects both through its function as a physical barrier and via yet to be defined mechanisms. At high concentrations of cytokines, these differences were abrogated, suggesting that the epithelium loses its protective effect under conditions of intense inflammation. These findings indicate that lung EpC may be protective or may downmodulate endothelial activation for neutrophil recruitment during pulmonary inflammation and this protection is mediated by barrier‐dependent and barrier‐independent mechanisms. Supported by NSHRF, IWK Health Centre and CIHR
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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".