Ex Vivo Adenoviral Vector Gene Delivery Results in Decreased Vector-associated Inflammation Pre- and Post–lung Transplantation in the Pig
Bibliographic record
Abstract
Acellular normothermic ex vivo lung perfusion (EVLP) is a novel method of donor lung preservation for transplantation. As cellular metabolism is preserved during perfusion, it represents a potential platform for effective gene transduction in donor lungs. We hypothesized that vector-associated inflammation would be reduced during ex vivo delivery due to isolation from the host immune system response. We compared ex vivo with in vivo intratracheal delivery of an E1-, E3-deleted adenoviral vector encoding either green fluorescent protein (GFP) or interleukin-10 (IL-10) to porcine lungs. Twelve hours after delivery, the lung was transplanted and the post-transplant function assessed. We identified significant transgene expression by 12 hours in both in vivo and ex vivo delivered groups. Lung function remained excellent in all ex vivo groups after viral vector delivery; however, as expected, lung function decreased in the in vivo delivered adenovirus vector encoding GFP (AdGFP) group with corresponding increases in IL-1β levels. Transplanted lung function was excellent in the ex vivo transduced lungs and inferior lung function was seen in the in vivo group after transplantation. In summary, ex vivo delivery of adenoviral gene therapy to the donor lung is superior to in vivo delivery in that it leads to less vector-associated inflammation and provides superior post-transplant lung function.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".