Human induced pluripotent stem cells for in vitro modeling of impaired mucociliary clearance in cystic fibrosis lung disease
Bibliographic record
Abstract
Abstract Severely impaired mucociliary airway function is the primary pathomechanism in Cystic Fibrosis (CF) lung disease. Despite significant advances in CF therapy, there is still a critical need for alternative, individualized treatment options, especially for patients with untreatable CFTR mutations. Although intestinal organoids and primary airway cells are widely used as preclinical models of CF, both systems exhibit limitations with regard to the proper modelling of mucociliary clearance or the availability of sufficient cell quantities. Patient-specific human induced pluripotent stem cells (hiPSCs) are a promising alternative due to their unlimited expansion potential and capacity to differentiate into airway epithelia. However, cellular inhomogeneities in iPSC-derived airway cultures complicated conventional assays that determine CFTR function such as Ussing chamber measurements, and a comprehensive demonstration of CF pathophysiology in hiPSC-derived airway models has been largely lacking. This study provides comprehensive data demonstrating very similar gene expression, (ultra)structure and CFTR function in CF iPSC-derived airway (iALI) and primary airway (pALI) cultures. Addressing current limitations, we have implemented a sensitive, straightforward, and automatable ciliary beat frequency (CBF) assay, which is largely unaffected by inhomogeneities and directly reflects disturbed mucus viscosity and mucociliary transport in CF lung disease. Electron microscopy images confirmed the disease phenotype showing a highly dense and dehydrated mucus layer on top of CF iALI cultures. Furthermore, established CFTR modulator drugs partially rescued the disease phenotype in CF iALI cultures, which validated the utility of iALI cultures as a scalable, patient-specific platform for CF research and personalized drug development.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".