Efficient Gene Delivery to Pig Airway Epithelia and Submucosal Glands Using Helper-Dependent Adenoviral Vectors
Bibliographic record
Abstract
Airway gene delivery is a promising strategy to treat patients with life-threatening lung diseases such as cystic fibrosis (CF). However, this strategy has to be evaluated in large animal preclinical studies in order to translate it to human applications. Because of anatomic and physiological similarities between the human and pig lungs, we utilized pig as a large animal model to examine the safety and efficiency of airway gene delivery with helper-dependent adenoviral vectors. Helper-dependent vectors carrying human CFTR or reporter gene LacZ were aerosolized intratracheally into pigs under bronchoscopic guidance. We found that the LacZ reporter and hCFTR transgene products were efficiently expressed in lung airway epithelial cells. The transgene vectors with this delivery can also reach to submucosal glands. Moreover, the hCFTR transgene protein localized to the apical membrane of both ciliated and nonciliated epithelial cells, mirroring the location of wild-type CF transmembrane conductance regulator (CFTR). Aerosol delivery procedure was well tolerated by pigs without showing systemic toxicity based on the limited number of pigs tested. These results provide important insights into developing clinical strategies for human CF lung gene therapy.Molecular Therapy-Nucleic Acids (2013) 2, e127; doi:10.1038/mtna.2013.55; published online 8 October 2013.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".