Easy-to-Build and Reusable Microfluidic Device for the Dynamic Culture of Human Bronchial Cystic Fibrosis Epithelia
Bibliographic record
Abstract
Cystic fibrosis (CF) is one of the most frequent genetic diseases, caused by dysfunction of the CF transmembrane conductance regulator (CFTR) chloride channel. CF particularly affects the epithelium of the respiratory system. Therapies aim at rescuing CFTR defects in the epithelium, but CF genetic heterogeneity hinders the finding of a single and generally effective treatment. Therefore, in vitro models have been developed to study CF and guide patient therapy. Here, we show a CF model on-chip by coupling the feasibility of the human bronchial epithelium differentiated in vitro at the air-liquid interface and the innovation of microfluidics. We demonstrate that the dynamic flow enhanced cilia distribution and increased mucus quantity, thus promoting tissue differentiation in a short time. The microfluidic devices highlighted differences between CF and non-CF epithelia, as shown by electrophysiological measures, mucus quantity, viscosity, and the analysis of ciliary beat frequency. The described model on-chip may be a handy instrument for studying CF and setting up therapies. As a proof of principle, we administrated the corrector VX-809 on-chip and observed a decrease in mucus thickness and viscosity.
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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".