Dynamic Metabolic Changes During Prolonged Ex Situ Heart Perfusion Are Associated With Myocardial Functional Decline
Bibliographic record
Abstract
heart perfusion (ESHP) was developed to preserve and evaluate donated hearts in a perfused beating state. However, myocardial function declines during ESHP, which limits the duration of perfusion and the potential to expand the donor pool. In this research, we combine a novel, minimally-invasive sampling approach with comparative global metabolite profiling to evaluate changes in the metabolomic patterns associated with declines in myocardial function during ESHP. Biocompatible solid-phase microextraction (SPME) microprobes serving as chemical biopsy were used to sample heart tissue and perfusate in a translational porcine ESHP model and a small cohort of clinical cases. In addition, six core-needle biopsies of the left ventricular wall were collected to compare the performance of our SPME sampling method against that of traditional tissue-collection. Our state-of-the-art metabolomics platform allowed us to identify a large number of significantly altered metabolites and lipid species that presented comparable profile of alterations to conventional biopsies. However, significant discrepancies in the pool of identified analytes using two sampling methods (SPME vs. biopsy) were also identified concerning mainly compounds susceptible to dynamic biotransformation and most likely being a result of low-invasive nature of SPME. Overall, our results revealed striking metabolic alterations during prolonged 8h-ESHP associated with uncontrolled inflammation not counterbalanced by resolution, endothelial injury, accelerated mitochondrial oxidative stress, the disruption of mitochondrial bioenergetics, and the accumulation of harmful lipid species. In conclusion, the combination of perfusion parameters and metabolomics can uncover various mechanisms of organ injury and recovery, which can help differentiate between donor hearts that are transplantable from those that should be discarded.
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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 | 0.001 |
| 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".