Interleukin-8 Release during Early Reperfusion Predicts Graft Function in Human Lung Transplantation
Bibliographic record
Abstract
Cytokines have been shown to play an important role in promoting inflammation in the setting of ischemia-reperfusion injury. However, their role in human lung transplantation has not been systematically explored. This study was undertaken to examine the kinetics of cytokine release in 18 consecutive human lung transplantation procedures and to examine the relationships between their levels and donor factors, length of ischemic time, and allograft function. TNF-alpha, IFN-gamma, IL-10, IL-12, and IL-18 were found at higher levels during the ischemic time, whereas IL-8 predominantly increased after reperfusion. IL-8 levels after 2 h of reperfusion correlated with lung function assessed by the Pa(O2 )/FI(O(2)) ratio, the mean airway pressure, and the APACHE score during the first 24 postoperative hours. The length of ICU stay also correlated with IL-8 levels after 2 h of reperfusion. Longer ischemic time was associated with significantly higher levels of IL-18 before reperfusion, and older donors had significantly lower levels of IL-10 after reperfusion. We have demonstrated the importance of IL-8 in predicting early graft function after human lung transplantation. In addition, we showed that donor age and ischemic time may influence release of specific cytokines during ischemia-reperfusion.
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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 | 0.002 |
| 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 | 0.001 | 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".