MODULATION OF ENDOTOXIN-INDUCED NEUTROPHIL TRANSENDOTHELIAL MIGRATION BY ALVEOLAR EPITHELIUM IN A DEFINED BILAYER MODEL
Bibliographic record
Abstract
Within the alveolus, epithelial cells, due to their close association with endothelial cells, can potentially influence endothelial cell responsiveness during inflammation and their interaction with leukocytes. To investigate this, three lung epithelial cell lines (A549, Calu-3, or NCI-H441) were grown with endothelium on opposing surfaces of Transwell filters and the formation and stability of bilayers was rigorously evaluated. All epithelial lines disrupted endothelial monolayer formation on filters with 3- or 5-microm pores by breaching the filter, and this occurred regardless of seeding density, matrix composition, or duration of culture. Endothelial disruption was not detectable by electrical resistance or permeability measurements but required cell-specific staining with immunofluorescence and microscopy. Distinct bilayers formed only on filters with 0.4-microm pores and only with A549 cells and human umbilical vein endothelial cells. Endotoxin (lipopolysaccharide [LPS]) stimulation of bilayers (4 hours) enhanced neutrophil transendothelial migration, but this was significantly decreased compared with the response of endothelium grown alone, irrespective of whether LPS exposure was via the epithelial or endothelial side of the bilayer. Down-modulation required epithelial-endothelial approximation and was not seen when these cells were separated by 0.5 to 1 mm. This study defines optimal conditions required for generation of intact bilayers of lung epithelial cells with endothelium for the study of leukocyte-transendothelial migration. Furthermore, it was demonstrated that lung epithelial cells can modulate endothelial cell responsiveness to an environmental inflammatory stimulus such as LPS and thus may have an important role in minimizing excessive and deleterious neutrophilic inflammation in the lung alveolus.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".