Safety and Efficacy of <i>Ex Vivo</i> Donor Lung Adenoviral IL-10 Gene Therapy in a Large Animal Lung Transplant Survival Model
Bibliographic record
Abstract
Ex vivo normothermic lung perfusion (EVLP) is a novel platform and method developed to facilitate functional assessment and implementation of advanced therapies for donor lungs prior to transplantation. This study aimed to determine the safety and immunological and functional benefits of ex vivo adenoviral human interleukin-10 (AdhIL-10) gene delivery to prevent the development of primary graft dysfunction in a large animal survival model. Pig donor lungs were retrieved, preserved for 6 h at 4°C, and then randomly assigned to four groups: (1) AdhIL-10 gene therapy: 12 h EVLP + AdhIL-10 intra-bronchial delivery; (2) EVLP-control: 12 h EVLP; (3) Vector-control: 12 h EVLP + adenoviral vector intra-bronchial delivery; and (4) prolonged hypothermic preservation: additional 12 h of cold ischemia. The left lung was then transplanted and evaluated. The recipients were recovered and kept alive until day 7 post-transplant under standard triple immunosuppression. Plasma levels of hIL-10 were detected in the treatment group throughout the 7 days. Analysis of peripheral blood obtained after transplant showed no signs of hematological, renal, or hepatic toxicity in the AdhIL-10 group. The immediate post-transplant lung function was significantly better in the EVLP-control and AdhIL-10 groups. Gas exchange at day 7 was superior in allografts from the AdhIL-10 group, and the histologic inflammation score was significantly lower. Lymphocytes from AdhIL-10 group harvested from mediastinal lymph nodes at day 7 post-transplantation and co-cultured with donor lymphocytes showed significantly less interferon gamma production in an Enzyme-Linked ImmunoSpot assay when compared with non-treatment groups. It has been demonstrated in this preclinical large animal survival study that ex vivo treatment with AdhIL-10 is safe and improves post-transplant lung function over EVLP alone. Improved function and an immunological advantage in both the innate and adaptive immune responses have been demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".