Novel therapeutic strategies for renal graft preservation and their potential impact on the future of clinical transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: The current review aims to examine recent evidence about improvements, therapeutics and novel approaches for renal graft preservation along with presenting a pragmatic outlook on their potential for clinical translation. RECENT FINDINGS: Modifying established cold preservation methods (4 °C) with oxygenation, gene therapies and gasotransmitters such as hydrogen sulfide has been shown to improve renal graft outcomes with minimum modifications to current protocols. These strategies have also shown promise in the context of normothermic preservation (34-37 °C), which circumvents the damage caused by cold preservation. Although normothermic machine perfusion (NMP) is being evaluated in clinical trials, it is limited by high cost, the use of blood and the lack of standardized protocols. Recent studies confirmed that preservation at subnormothermic temperatures (∼20 °C) is effective with approved preservation solutions and, in conjunction with exogenous hydrogen sulfide therapy, this approach may expedite a static preservation alternative to NMP. SUMMARY: Progress has been made in investigating improvements and alternatives to cold preservation. Promising therapeutic strategies have also been studied in the context of cold, subnormothermic and normothermic preservation. Further research is needed to optimize clinical renal graft preservation.
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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) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".