Protection of the Transplant Kidney from Preservation Injury by Inhibition of Matrix Metalloproteinases
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs), particularly MMP-2 and MMP-9, play an important role in ischemic injury to the heart, yet it is not known if these MMPs are involved in the injury that occurs to the transplant kidney. We therefore studied the pharmacologic protection of transplant kidneys during machine cold perfusion. METHODS: Human kidney perfusates were analyzed for the presence of injury markers such as cytochrome c oxidase, lactate dehydrogenase, and neutrophil-gelatinase associated lipocalin (NGAL), and MMP-2 and MMP-9 were measured. The effects of MMP inhibitors MMP-2 siRNA and doxycycline were studied in an animal model of donation after circulatory determination of death (DCDD). RESULTS: Markers of injury were present in all analyzed perfusates, with higher levels seen in perfusates from human kidneys donated after controlled DCDD compared to brain death and in perfusate from kidneys with delayed graft function. When rat kidneys were perfused at 4°C for 22 hours with the addition of MMP inhibitors, this resulted in markedly reduced levels of MMP-2, MMP-9 and analyzed injury markers. CONCLUSIONS: Based on our study, MMPs are involved in preservation injury and the supplementation of preservation solution with MMP inhibitors is a potential novel strategy in protecting the transplant kidney from preservation injury.
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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".