Ex vivo delivery of recombinant IL-10 to human donor lungs
Bibliographic record
Abstract
Background: The immunoregulatory cytokine interleukin-10 (IL-10) has been shown to be a promising therapy for donor lung injuries before transplantation. However, the very short half-life of IL-10 in vivo (∼2 hours) has necessitated the use of gene therapy in almost all animal models of lung transplantation. Because isolation of the donor lung on the ex vivo lung perfusion (EVLP) circuit removes it from the influence of renal and hepatic clearance mechanisms, a much-prolonged half-life of IL-10 is anticipated. Thus, we hypothesized that delivery of recombinant IL-10 (rIL-10) to injured donor lungs isolated on EVLP could be a clinically relevant and a logistically simpler method of employing IL-10 therapy in lung transplantation. Methods: = 5/group) to receive either saline (control), rIL-10 (5 µg in 2-liter perfusate), or rIL-10 (25 µg) aerosolized into the airways. Results: Perfusate and intratracheal delivery of rIL-10 did not provide the therapeutic anti-inflammatory action that has been traditionally achieved with gene therapy. It appears that intratracheally delivered rIL-10 moves into the perfusate where it seems to be biologically inactive. Conclusions: Gene therapy remains superior as it allows for continued production of IL-10 within the alveoli where it has the potential to continuously act on alveolar macrophages and epithelial cells in a paracrine fashion.
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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 | 0.000 |
| 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".