Functional Repair of Human Donor Lungs by IL-10 Gene Therapy
Bibliographic record
Abstract
More than 80% of potential donor lungs are injured during brain death of the donor and from complications experienced in the intensive care unit, and therefore cannot be used for transplantation. These lungs show inflammation and disruption of the alveolar-capillary barrier, leading to poor gas exchange. Although the number of patients in need of lung transplantation is increasing, the number of donors is static. We investigated the potential to use gene therapy with an adenoviral vector encoding human interleukin-10 (AdhIL-10) to repair injured donor lungs ex vivo before transplantation. IL-10 is an anti-inflammatory cytokine that mainly exerts its suppressive functions by the inactivation of antigen-presenting cells with consequent inhibition of proinflammatory cytokine secretion. In pigs, AdhIL-10-treated lungs exhibited attenuated inflammation and improved function after transplantation. Lungs from 10 human multiorgan donors that had suffered brain death were determined to be clinically unsuitable for transplantation. They were then maintained for 12 hours at body temperature in an ex vivo lung perfusion system with or without intra-airway delivery of AdhIL-10 gene therapy. AdhIL-10-treated lungs showed significant improvement in function (arterial oxygen pressure and pulmonary vascular resistance) when compared to controls, a favorable shift from proinflammatory to anti-inflammatory cytokine expression, and recovery of alveolar-blood barrier integrity. Thus, treatment of injured human donor lungs with the cytokine IL-10 can improve lung function, potentially rendering injured lungs suitable for transplantation into patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".