Porcine model of sepsis-induced systemic inflammation and acute lung injury in donor lungs
Bibliographic record
Abstract
Abstract Background: The shortage of organ donors is a major challenge in lung transplantation. To expand the lung donor pool, ex-vivo lung perfusion (EVLP) has emerged as a platform for assessment and reconditioning marginal donor lungs. In this study a stable and reproducible large animal model of lipopolysaccharide (LPS) induced systemic inflammation and acute lung injury (ALI) was developed.Methods: Pigs (n=6) were anesthetized and monitored. After infusion of LPS (20 μg/kg) for 1 hour, followed by a 90-minute response period, lungs were procured and kept on ice for 2 hours, followed by 4 hours of EVLP. Pulmonary function, inflammatory biomarkers and edema formation were measured in vivo before procurement and during EVLP. Pro and anti-inflammatory cytokines were assayed in blood and in EVLP perfusate, which were collected before and every 30 minutes after LPS administration and EVLP.Results: LPS infusion resulted in significant hemodynamic instability, characterized by marked pulmonary hypertension, decreased systemic blood pressure and increased heart rate. This was associated with increased levels of TNFα, IL-10, IL-6, but no change in IL-1β. Ex vivo assessment of injured lungs showed graft dysfunction characterized by impaired gas exchange and edema formation. The inflammatory profile showed stable but elevated TNFα levels, and continuous production of interleukins during EVLP.Conclusion: We describe a reproducible large animal model of LPS-induced systemic inflammation and ALI. EVLP alone was unable to recondition severely injured lungs. These findings suggest that the EVLP platform requires adjuncts such as targeted anti-inflammatory agents to allow reconditioning of marginal donor lungs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 0.002 |
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".