Collagen Tubular Airway-on-Chip for Extended Epithelial Culture and Investigation of Ventilation Dynamics
Bibliographic record
Abstract
Abstract The lower respiratory tract is a hierarchical network of compliant tubular structures that are made from extracellular matrix proteins with a wall lined by an epithelium. While microfluidic airway-on-a-chip models incorporate the effects of shear and stretch on the epithelium, week-long air-liquid-interface (ALI) culture remains limited to static conditions. The circular cross-section and substrate compliance associated with intact airways have yet to be recapitulated to allow studies of epithelial injuries under physiological and ventilation conditions. To overcome these limitations, we present a collagen tube-based airway model. Sustaining a functional human bronchial epithelium during two-week perfusion is accomplished by continuously supplying warm, humid air at the apical side and culture medium at the basal side. The model faithfully recapitulates human airways in size, composition, and mechanical microenvironment, allowing for the first time dynamic studies of elastocapillary phenomena associated with regular breathing as well as mechanical ventilation, along with the impact on epithelial cells. Findings reveal the epithelium to become increasingly damaged when subjected to repetitive collapse and reopening as opposed to overdistension and suggest expiratory flow resistance to reduce atelectasis. We expect the model to find broad potential applications in organ-on-a-chip applications for various tubular tissues.
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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) | 0.001 | 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".