PROSTAGLANDIN E1 PROTECTS LUNG TRANSPLANTS FROM ISCHEMIA-REPERFUSION INJURY: A SHIFT FROM PRO- TO ANTI-INFLAMMATORY CYTOKINES1
Bibliographic record
Abstract
INTRODUCTION: Prostaglandin E1 (PGE1) has been demonstrated to reduce ischemia-reperfusion (IR) injury following lung transplantation. However, the cytoprotective mechanisms remain largely unknown. The purpose of this study was to determine whether the mechanism through which PGE1 improves IR injury is related to the level of apoptosis or the release of inflammatory cytokines. METHODS: In a rat single-lung-transplant model, animals were randomly allocated into four groups of five animals each. Group 1 received normal saline (NS) in the preservation solution and during the 2-hr reperfusion period. Group 2 received NS in the preservation solution and PGE1 during the reperfusion period. Group 3 received PGE1 in the preservation solution and NS during the reperfusion period. Group 4 received PGE1 in the preservation solution and during the reperfusion period. RESULTS: The two groups that received PGE1 during the reperfusion period had a significantly higher partial pressure of oxygen (PaO2), lower wet-dry weight ratio, and lower peak airway pressure at the end of the reperfusion period than did the two groups that received NS. In the two groups that received PGE1 during the reperfusion period, we observed significantly higher levels of interleukin (IL)-10 in the transplanted lung tissue and plasma and significantly lower levels of tumor necrosis factor (TNF)-alpha, interferon (IFN)-gamma, and IL-12 in lung tissue. The levels of IL-4 and macrophage inflammatory protein-2 (MIP-2) were not significantly different between groups. The number of apoptotic cells and the expression of Bcl-2 were not significantly different between groups. CONCLUSIONS: PGE1 does not decrease the amount of apoptosis after reperfusion and does not significantly upregulate Bcl-2. We have demonstrated that PGE1 administered during the reperfusion period reduces IR injury and improves lung function through a mechanism that is likely mediated by a shift between pro- and anti-inflammatory cytokine release.
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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) | 0.002 | 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".