Endothelin Receptor Antagonist SB209670 Decreases Lung Allograft Apoptosis and Improves Lung Graft Function After Prolonged Ischemia
Bibliographic record
Abstract
Apoptosis has been postulated as a contributing factor in ischemia-reperfusion graft dysfunction following lung transplantation. The purpose of this study was to determine whether treatment with an endothelin-A/endothelin-B- (ET(A)/ET(B)) receptor antagonist could reduce the level of apoptosis observed in the lung following ischemia-reperfusion injury. Eleven dogs were subjected to left lung allotransplantation. Heart-lung blocks were harvested from donor dogs and preserved with modified Eurocollins solution and stored at 4 degrees C for 18 to 20 h. We investigated the level of apoptosis by terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL), in the lungs of animals receiving an intravenous infusion of saline (control, n = 5) or the ET receptor antagonist SB209670 (n = 6) (15 microg/kg/min). The infusion began 30 min prior to transplantation and continued for up to 6 h thereafter. The TUNEL staining was significantly higher in the airway epithelium and the parenchyma of the saline (control) group (40.67 +/- 6.16), compared with native right lungs (5.00 +/- 0.56) and the treatment group (14.13 +/- 2.84). We conclude that treatment of lung allografts with the mixed ET(A)/ET(B)-receptor antagonist SB209670 can ameliorate lung injury by reducing the level of apoptosis seen in the allograft following ischemia-reperfusion injury.
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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) | 0.001 | 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".