An Optimized <i>In Vitro</i> Model of the Respiratory Tract Wall to Study Particle Cell Interactions
Bibliographic record
Abstract
As a part of the respiratory tissue barrier, lung epithelial cells play an important role against the penetration of the body by inhaled particulate foreign materials. In most cell culture models, which are designed to study particle-cell interactions, the cells are immersed in medium. This does not reflect the physiological condition of lung epithelial cells which are exposed to air, separated from it only by a very thin liquid lining layer with a surfactant film at the air-liquid interface. In this study, A549 epithelial cells were grown on microporous membranes in a two chamber system. After the formation of a confluent monolayer the cells were exposed to air. The morphology of the cells and the expression of tight junction proteins were studied with confocal laser scanning and transmission electron microscopy. Air-exposed cells maintained monolayer structure for 2 days, expressed tight junctions and developed transepithelial electrical resistance. Surfactant was produced and released at the apical side of the air-exposed epithelial cells. In order to study particle-cell interactions fluorescent 1 microm polystyrene particles were sprayed over the epithelial surface. After 4 h, 8.8% of particles were found inside the epithelium. This fraction increased to 38% after 24 h. During all observations, particles were always found in the cells but never between them. In this study, we present an in vitro model of the respiratory tract wall consisting of air-exposed lung epithelial cells covered by a liquid lining layer with a surfactant film to study particle-cell interactions.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".