Creating superior lungs for transplantation with next-generation gene therapy during ex vivo lung perfusion
Bibliographic record
Abstract
Engineering donor organs to better tolerate the harmful non-immunological and immunological responses inherently related to solid organ transplantation would improve transplant outcomes. Our enhanced knowledge of ischemia-reperfusion injury, alloimmune responses and pathological fibroproliferation after organ transplantation, and the advanced toolkit available for gene therapies, have brought this goal closer to clinical reality. Ex vivo organ perfusion has evolved rapidly especially in the field of lung transplantation, where clinicians routinely use ex vivo lung perfusion (EVLP) to confirm the quality of marginal donor lungs before transplantation, enabling safe transplantation of organs originally considered unusable. EVLP would also be an attractive platform to deliver gene therapies, as treatments could be administered to an isolated organ before transplantation, thereby providing a window for sophisticated organ engineering while minimizing off-target effects to the recipient. Here, we review the status of lung transplant first-generation gene therapies that focus on inducing transgene expression in the target cells. We also highlight recent advances in next-generation gene therapies, that enable gene editing and epigenetic engineering, that could be used to permanently change the donor organ genome and to induce widespread transcriptional gene expression modulation in the donor lung. In a future vision, dedicated organ repair and engineering centers will use gene editing and epigenetic engineering, to not only increase the donor organ pool, but to create superior organs that will function better and longer in the recipient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".