Subnormothermic Machine Perfusion of Neonatal and Small-Sized Pediatric Donor Hearts
Bibliographic record
Abstract
Donor heart machine perfusion enables avoidance of prolonged cold ischemia, however the optimal temperature is yet to be elucidated. Given that maintenance of temperature beyond ambient levels demands significant energy, we sought to determine the suitability of room-temperature perfusion preservation of neonatal/pediatric-sized (5-20 kg) piglet donor hearts. A custom device was fabricated suitable for this purpose, with continuous readout of perfusion pressure, flow rate, temperature, and oxygen saturation. Oxygen delivery was automated to keep saturation above 90%. The perfusate consisted of a 1:1 mix of donor whole blood and modified Krebs-Henseleit solution with albumin. Donor hearts were procured from 5 kg (n = 5), 10 kg (n = 3), and 20 kg (n = 5) piglets, and perfused for 10 hours. Subsequently, 20 kg piglet hearts were transplanted. Hemodynamic stability and echocardiographic measurement of donor heart function were evaluated posttransplant. Perfusate parameters were stable through the perfusion interval. Temperature was consistently 23.8 ± 1.6°C. pH (7.35 ± 0.09) and pO 2 (102 ± 29 mm Hg) were steady throughout. Glucose (5.9 ± 1.8 mmol/L) and lactate (2.4 ± 1.5 mmol/L) were metabolized by the heart over the course of perfusion. Transplanted hearts displayed durable hemodynamics and good biventricular function. We conclude that neonatal and pediatric hearts can be safely perfused for extended periods at subnormothermic conditions using blood-based perfusate.
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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 | 0.000 |
| 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) | 0.001 | 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".