The endogenous capacity to produce proinflammatory mediators by the ex vivo human perfused lung
Bibliographic record
Abstract
Abstract Background The ex vivo human perfused lung model has enabled optimizing donor lungs for transplantation and delineating mechanisms of lung injury. Perfusate and airspace biomarkers are a proxy of the lung response to experimental conditions. However, there is a lack of studies evaluating biomarker kinetics during perfusion and after exposure to stimuli. In this study, we analyzed the ex vivo-perfused lung response to three key perturbations: exposure to the perfusion circuit, exogenous fresh whole blood, and bacteria. Results Ninety-nine lungs rejected for transplantation underwent ex vivo perfusion. One hour after reaching experimental conditions, fresh whole blood was added to the perfusate ( n = 55). Two hours after reaching target temperature, Streptococcus pneumoniae was added to the perfusate ( n = 42) or to the airspaces ( n = 17). Perfusate and airspace samples were collected at baseline (once lungs were equilibrated for 1 h, but before blood or bacteria were added) and 4 h later. Interleukin (IL)-6, IL-8, angiopoietin (Ang)-2, and soluble tumor necrosis factor receptor (sTNFR)-1 were quantified. Baseline perfusate and airspace biomarker levels varied significantly, and this was not related to pre-procurement P a O 2 :FiO 2 ratio, cold ischemia time, and baseline alveolar fluid clearance (AFC). After 4 h of ex vivo perfusion, the lung demonstrated a sustained production of proinflammatory mediators. The change in biomarker levels was not influenced by baseline donor lung characteristics (cold ischemia time, baseline AFC) nor was it associated with measures of experimental epithelial (final AFC) or endothelial (percent weight gain) injury. In the presence of exogenous blood, the rise in biomarkers was attenuated. Lungs exposed to intravenous (IV) bacteria relative to control lungs demonstrated a significantly higher rise in perfusate IL-6. Conclusions The ex vivo-perfused lung has a marked endogenous capacity to produce inflammatory mediators over the course of short-term perfusion that is not significantly influenced by donor lung characteristics or the presence of exogenous blood, and only minimally affected by the introduction of systemic bacteremia. The lack of association between biomarker change and donor lung cold ischemia time, final alveolar fluid clearance, and experimental percent weight gain suggests that the maintained ability of the human lung to produce biomarkers is not merely a marker of lung epithelial or endothelial injury, but may support the function of the lung as an immune cell reservoir.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".