Oligonucleotide-based Preconditioning of DCD Cardiac Donors and Its Impact on Cardiac Viability
Bibliographic record
Abstract
BACKGROUND: While clinical donation after circulatory death (DCD) cardiac transplantation is being implemented with increasing frequency to address the supply/demand mismatch of donor grafts, no research to date has examined a strategy of donor preconditioning to optimize the viability of DCD hearts for transplantation. In our rat model of the DCD protocol, we investigate the impact of pretreating donors with phosphorothioate-linked cytosine and guanine rich oligodeoxynucleotides (CpG ODN) and their effects on cardiac function, injury, and a novel left ventricular (LV) mRNA biomarker panel. METHODS: DCD rats were subjected to a withdrawal protocol, followed by 20 minutes of warm acirculatory standoff, representing a group of severely injured hearts as previously demonstrated. Beating heart controls and DCD rats were pretreated with vehicle or stimulatory CpG ODN (beating heart control and DCD stimulated with CpG ODN, BST and DST). Hearts were harvested for ex situ heart perfusion (ESHP), where LV function, histochemical injury, and differences in gene expression were characterized between groups. RESULTS: Donor pretreatment with CpG ODN doubled the number of functional DCD hearts at ESHP. Pretreatment was associated with improved systolic and diastolic LV function, a reduction in histological injury, and markedly reduced elaboration of cardiac troponin-I in coronary effluent during ESHP. Pretreatment was also associated with a reduction in mRNA biomarkers associated with myocardial injury. CONCLUSIONS: A single dose of CpG ODN was associated with reduced biomarkers of cardiac injury and a 100% increase in cardiac viability in this rodent model of marginal DCD cardiac donation.
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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 | 0.000 |
| 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".