Transducing Airway Basal Cells with a Helper-Dependent Adenoviral Vector for Lung Gene Therapy
Bibliographic record
Abstract
A major challenge in developing gene-based therapies for airway diseases such as cystic fibrosis (CF) is sustaining therapeutic levels of transgene expression over time. This is largely due to airway epithelial cell turnover and the host immunogenicity to gene delivery vectors. Modern gene editing tools and delivery vehicles hold great potential for overcoming this challenge. There is currently not much known about how to deliver genes into airway stem cells, of which basal cells are the major type in human airways. In this study, helper-dependent adenoviral (HD-Ad) vectors were delivered to mouse and pig airways via intranasal delivery, and direct bronchoscopic instillation, respectively. Vector transduction was assessed by immunostaining of lung tissue sections, which revealed that airway basal cells of mice and pigs can be targeted in vivo. In addition, efficient transduction of primary human airway basal cells was verified with an HD-Ad vector expressing green fluorescent protein. Furthermore, we successfully delivered the human CFTR gene to airway basal cells from CF patients, and demonstrated restoration of CFTR channel activity following cell differentiation in air-liquid interface culture. Our results provide a strong rationale for utilizing HD-Ad vectors to target airway basal cells for permanent gene correction of genetic airway diseases.
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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 | 0.001 |
| 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".