Dialysis preserves heart function during ex situ heart perfusion
Bibliographic record
Abstract
Background Ex situ heart perfusion (ESHP) has been used to optimize donor organs before heart transplantation. However, cardiac function often deteriorates with the development of myocardial edema. The use of dialysis during ESHP could assist in cardiac preservation. Methods Male Yorkshire pig hearts were subjected to ESHP for 8 hours with or without dialysis. Hearts were supported during nonworking mode and working mode, and pressure-volume loops and coronary vasomotor function were evaluated. Finally, tissue biopsies were assessed for mitochondrial function, oxidative stress, and inflammation. Results Adding dialysis to ESHP significantly enhanced cardiac function, with improved preload recruitable stroke work at 4 hours (64.09 ± 20.13 vs 35.08 ± 13.52, p = 0.010) and 8 hours (64.31 ± 9.08 vs 23.30 ± 19.25, p = 0.0002), maximal elastance at 8 hours (24.67 ± 10.75 vs 10.62 ± 8.471, p = 0.0477), and end diastolic pressure volume relationship at 8 hours (644.7 ± 566.68 vs 86.63 ± 72.05, p = 0.0187). Coronary vasomotor function improved in the dialysis group in endothelium dependent (LogIC50 −7.39 ± 0.25 vs −2.22 ± 0.76, p < 0.0001) and independent (LogIC50 −6.11 ± 0.19 vs −4.79 ± 0.11, p < 0.0001) vasorelaxation. Dialyzed hearts also had reduced sensitivity to endothelin-1 (LogEC50 −7.94 ± 0.5 vs −8.54 ± 0.06, p = 0.0449) and significant changes in endothelin receptor-related protein expression related and oxidative stress. Conclusions The combination of dialysis with ESHP improves myocardial and coronary vasomotor preservation and may allow for longer perfusion times.
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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".