Advances in machine perfusion, organ preservation, and cryobiology: potential impact on vascularized composite allotransplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: In this review, we discuss novel strategies that allow for extended preservation of vascularized composite allografts and their potential future clinical implications for the field of vascularized composite allotransplantation (VCA). RECENT FINDINGS: The current gold standard in tissue preservation - static cold preservation on ice - is insufficient to preserve VCA grafts for more than a few hours. Advancements in the field of VCA regarding matching and allocation, desensitization, and potential tolerance induction are all within reasonable reach to achieve; these are, however, constrained by limited preservation time of VCA grafts. Although machine perfusion holds many advantages over static cold preservation, it currently does not elongate the preservation time. More extreme preservation techniques, such as cryopreservation approaches, are, however, specifically difficult to apply to composite tissues as the susceptibility to ischemia and cryoprotectant agents varies greatly by tissue type. SUMMARY: In the current scope of extended preservation protocols, high subzero approaches of VCA grafts will be particularly critical enabling technologies for the implementation of tolerance protocols clinically. Ultimately, advances in both preservation techniques and tolerance induction have the potential to transform the field of VCA and eventually lead to broad applications in reconstructive transplantation.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".