A microfluidic device to apply shear stresses to polarizing ciliated airway epithelium using air flow
Bibliographic record
Abstract
Organization of airway epithelium determines ciliary beat direction and coordination for proper mucociliary clearance. Fluidic shear stresses have the potential to influence ciliary organization. Here, an in vitro fluidic flow system was developed for inducing long-term airflow shear stresses on airway epithelium with a view to influencing epithelial organization. Our system consists of a fluidic device for cell culture, integrated into a humidified airflow circuit. The fluidic device has a modular design and is made from a combination of polystyrene and adhesive components incorporated into a 6-well filter membrane insert. We demonstrate the system operates within physiologically relevant shear and pressure ranges and estimate the shear stress exerted on the epithelial cell layer as a result of air flow using a computational model. For both the bronchial epithelial cell line BEAS2B and primary human tracheal airway epithelial cells, we demonstrate that cells remain viable within the device when exposed to airflow for 24 h and that normal differentiation and cilia formation occurs. Furthermore, we demonstrate the utility of our device for exploring the impact of exposing cells to airflow: our tool enables quantification of cytoskeletal organization, and is compatible with in situ bead assays to assess the orientation of cilia beating.
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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 | 0.001 | 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".