Impact of Human Interleukin-10 on Vector-Induced Inflammation and Early Graft Function in Rat Lung Transplantation
Bibliographic record
Abstract
This study was undertaken to examine the time course of human interleukin (hIL)-10 gene expression after transtracheal administration of adenoviral (Ad)hIL-10 and its effect on the early adenoviral proinflammatory cytokine response and on post-transplant lung function. Using a rat lung transplant model, we observed that lungs retrieved 12 h after the administration of AdhIL-10 were associated with significant improvement in post-transplant lung function. Shorter periods of transfection were associated with significantly elevated levels of tumor necrosis factor-alpha and macrophage inflammatory protein-2 in lung tissue, leading to an increased degree of injury. The release of proinflammatory cytokines secondary to the adenoviral vector was reduced by high-dose methylprednisolone (30 mg/kg) administered 3 h before transfection. Reduction in the early adenoviral inflammatory response was associated with significant improvement in post-transplant lung function when lungs were retrieved 6 or 12 h after transtracheal administration of AdhIL-10. Transtracheal administration of adenoviral-mediated hIL-10 to donor lungs is associated with a significant early inflammatory response that may enhance ischemia-reperfusion injury if insufficient hIL-10 is expressed in lung tissue before retrieval. The period between delivery of AdhIL-10 and lung retrieval can be reduced if the early inflammatory response is suppressed with methylprednisolone.
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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 | 0.000 | 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".