Impact of machine perfusion on transplant infectious diseases: New challenges and opportunities
Bibliographic record
Abstract
Preservation techniques that maintain the viability of an organ graft between retrieval from the donor and implantation into the recipient remain a critical aspect of solid organ transplantation. While traditionally preservation is accomplished with static cold storage, advances in ex vivo dynamic machine perfusion, both hypothermic and normothermic, have allowed for prolongation of organ viability and recovery of marginal organs effectively increasing the usable donor pool. However, the use of these novel machine perfusion technologies likely exposes the recipient to additional infectious risk either through clonal expansion of pathogens derived during organ recovery or de novo exogenous acquisition of pathogens while the organ remains on the machine perfusion circuit. There is a paucity of high-quality studies that have attempted to quantify infection risk, although it appears that prolonging the time on the machine perfusion circuit and normothermic parameters increases the risk of infection. Conversely, the use of ex vivo machine perfusion unlocks new opportunities to detect and treat donor-derived infections before implantation into the recipient. This review seeks to reveal how the use of ex vivo machine perfusion strategies may augment the risk of infection in the organ recipient as well as outline ways that this technology could be leveraged to enhance our ability to manage donor-derived infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".