Pre-clinical Model of Cardiac Donation after Circulatory Death
Bibliographic record
Abstract
Cardiac transplantation demand is on the rise; nevertheless, organ availability is limited due to a paucity of suitable donors. Organ donation after circulatory death (DCD) is a solution to address this limited availability, but due to a period of prolonged warm ischemia and the risk of tissue injury, its routine use in cardiac transplantation is seldom seen. In this manuscript we provide a detailed protocol closely mimicking current clinical practices in the context of DCD with continuous monitoring of heart function, allowing for the evaluation of novel cardioprotective strategies and interventions to decrease ischemia-reperfusion injury. In this model, the DCD protocol is initiated in anesthetized Lewis rats by stopping ventilation to induce circulatory death. When systolic blood pressure drops below 30 mmHg, the warm ischemic time is initiated. After a pre-set warm ischemic period, hearts are flushed with a normothermic cardioplegic solution, procured, and mounted onto a Langendorff ex vivo heart perfusion system. Following 10 min of initial reperfusion and stabilization, cardiac reconditioning is continuously evaluated for 60 min using intraventricular pressure monitoring. A heart injury is assessed by measuring cardiac troponin T and the infarct size is quantified by histological staining. The warm ischemic time can be modulated and tailored to develop the desired amount of structural and functional damage. This simple protocol allows for the evaluation of different cardioprotective conditioning strategies introduced at the moment of cardioplegia, initial reperfusion and/or during ex vivo perfusion. Findings obtained from this protocol can be reproduced in large models, facilitating clinical translation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".