Cardioprotective Actions of a Glucagon‐like Peptide‐1 Receptor Agonist on Hearts Donated After Circulatory Death
Bibliographic record
Abstract
Background Heart transplantation with a donation after circulatory death (DCD) heart is complicated by substantial organ ischemia and ischemia–reperfusion injury. Exenatide, a glucagon‐like peptide−1 receptor agonist, manifests protection against cardiac ischemia–reperfusion injury in other settings. Here we evaluate the effects of exenatide on DCD hearts in juvenile pigs. Methods and Results DCD hearts with 15‐minutes of global warm ischemia after circulatory arrest were reperfused ex vivo and switched to working mode. Treatment with concentration 5‐nmol exenatide was given during reperfusion. DCD hearts treated with exenatide showed higher myocardial oxygen consumption (exenatide [n=7] versus controls [n=7], over 60–120 minutes of reperfusion, P <0.001) and lower cardiac troponin‐I release (27.94±11.17 versus 42.25±11.80 mmol/L, P =0.04) during reperfusion compared with controls. In working mode, exenatide‐treated hearts showed better diastolic function (dp/dt min: −3644±620 versus −2193±610 mm Hg/s, P <0.001; Tau: 15.62±1.78 versus 24.59±7.35 milliseconds, P =0.02; lateral e ′ velocity: 11.27 ± 1.46 versus 7.19±2.96, P =0.01), as well as lower venous lactate levels (3.17±0.75 versus 5.17±1.44 mmol/L, P =0.01) compared with controls. Higher levels of activated endothelial nitric oxide synthase (phosphorylated to total endothelial nitric oxide synthase levels: 2.71±1.16 versus 1.37±0.35, P =0.02) with less histological evidence of endothelial damage (von Willebrand factor expression: 0.024±0.007 versus 0.331±0.302, pixel/μm, P =0.04) was also observed with exenatide treatment versus controls. Conclusions Acute treatment of DCD hearts with exenatide limits myocardial and endothelial injury and improves donor cardiac function.
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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".