A Rodent Model of Cardiac Donation After Circulatory Death and Novel Biomarkers of Cardiac Viability During Ex Vivo Heart Perfusion
Bibliographic record
Abstract
BACKGROUND: Organ donation after circulatory death (DCD) is increasingly being used as a means of addressing the organ supply/demand mismatch in solid organ transplantation. There is reluctance to use DCD hearts, due to an inability to precisely identify hearts that have suffered irreversible injury. We investigated novel biomarkers and clinically relevant endpoints across a spectrum of warm ischemic times, before and during ex vivo heart perfusion (EVHP), to identify features associated with a nonviable cardiac phenotype. METHODS: Donor rats sustained a hypoxic cardiac arrest, followed by variable acirculatory standoff periods (DCD groups). Left ventricular function, histochemical injury, and differences in left ventricular gene expression were studied before, and during, EVHP. RESULTS: As warm ischemic time exposure increased in DCD groups, fewer hearts were functional during EVHP, and ventricular function was increasingly impaired. Histochemical assessment identified severely injured hearts during EVHP. A novel gene expression signature identified severely injured hearts during EVHP (upregulation of c-Jun, 3.19 (2.84-3.60); P = 0.0014; HMOX-1, 3.87 (2.72-5.50); P = 0.0037; and Hsp90, 7.66 (6.32-9.27); P < 0.0001 in DCD20), and may be useful in identifying high-risk hearts at the point of harvest (Hsp90). CONCLUSIONS: We demonstrate that our preclinical model recapitulates the cardio-respiratory decompensation observed in humans, and that EVHP appears necessary to unmask distinguishing features of severely injured DCD hearts. Furthermore, we outline a clinically relevant multimodal approach to assessing candidate DCD hearts. Novel mRNA signatures correlated with elevations in cardiac Troponin-I in severely injured hearts during EVHP, and may also detect injury at the point of harvest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".