Thixotropic Solutions Enhance Viral-Mediated Gene Transfer to Airway Epithelia
Bibliographic record
Abstract
Adenovirus-mediated gene transfer to airway epithelia is inefficient in part because its receptor is absent on the apical surface of the airways. Targeting adenovirus to other receptors, increasing the viral concentration, and even prolonging the incubation time with adenovirus vectors can partially overcome the lack of receptors and facilitate gene transfer. Unfortunately, mucociliary clearance would prevent prolonged incubation time in vivo. Thixotropic solutions (TS) are gels that upon a vigorous shearing force reversibly become liquid. We hypothesized that formulating recombinant adenoviruses in TS would decrease virus clearance and thus enhance gene transfer to the airway epithelia. We found that clearance of virus-sized fluorescent beads by human airway epithelia in vitro and by monkey trachea in vivo were markedly decreased when the beads were formulated in TS compared with phosphate-buffered saline (PBS). Adenovirus formulated in TS significantly increased adenovirus-mediated gene transfer of a reporter gene in human airway epithelia in vitro and in murine airway epithelia in vivo. Furthermore, an adenovirus encoding the cystic fibrosis transmembrane regulator (CFTR) gene (AdCFTR) formulated in TS was more efficient in correcting the chloride transport defect in cystic fibrosis airway epithelia than AdCFTR formulated in PBS. These data indicate a novel strategy to augment the efficiency of gene transfer to the airways that may be applicable to a number of different gene transfer vectors and could be of value in gene transfer to cystic fibrosis (CF) airway epithelia in vivo.
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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".