Exploring epithelial dysfunction in primary ciliary dyskinesia
Bibliographic record
Abstract
Background: Primary ciliary dyskinesia (PCD) is a rare autosomal recessive disorder characterized by mutations in ciliary genes that cause a dyskinetic ciliary beat pattern. This results in impaired mucociliary clearance, recurrent infections, and progressive lung damage, culminating in bronchiectasis. Despite these clinical manifestations, the epithelial biology and inflammatory response in PCD remain poorly understood. Aim: To investigate the composition and transcriptomic profile of the airway epithelium in PCD using in vitro cell culture. Method: Nasal brush biopsies from PCD patients with a static cilia phenotype and healthy age/sex-matched controls were differentiated in air-liquid interface (ALI) cultures (n=9). Basal, ciliated, and mucosecretory cells were quantified using qPCR, flow cytometry, and histology. Bulk RNA sequencing was performed, and cytokine levels in culture supernatants were measured using ELISAs. Results: Following differentiation, high-speed video microscopy was used to confirm all PCD ALI cultures had static cilia. PCD cultures contained fewer basal cells than controls, demonstrating a distinct differentiation capacity of the PCD epithelium in vitro. Bulk RNA sequencing revealed differential expression of NOS2, and pathway enrichment analysis showed altered cell cycle, wound healing and phosphoinositide 3-kinase (PI3K) signalling pathways within PCD cultures. Conclusion: We provide an RNA sequencing dataset for PCD, identifying reduced NOS2, consistent with reduced nasal nitric oxide in patients. PCD epithelium exhibits distinct differentiation, suggesting potential implications for basal cell function within PCD. The implication of these differences remains to be determined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".