Protection of the transplant kidney during cold perfusion with doxycycline: proteomic analysis in a rat model
Bibliographic record
Abstract
BACKGROUND: It has been previously shown that doxycycline (Doxy) protects the kidney from preservation injury by inhibition of matrix metalloproteinase. However, the precise molecular mechanism involved in this protection from injury is not known. We used a pharmaco-proteomics approach to identify potential molecular targets associated with kidney preservation injury. METHODS: Rat kidneys were cold perfused with or without doxycycline (Doxy) for 22 h. Kidneys perfusates were analyzed for the presence of injury markers such as lactate dehydrogenase (LDH), and neutrophil-gelatinase associated lipocalin (NGAL). Proteins extracted from kidney tissue were analyzed by 2-dimensional gel electrophoresis. Proteins of interest were identified by mass spectrometry. RESULTS: Triosephosphate isomerase, PGM, dihydropteridine reductase-2, pyridine nucleotide-disulfide oxidoreductase, phosphotriesterase-related protein, and aminoacylase-1A were not affected by cold perfusion. Perfusion with Doxy increased their levels. N(G),N(G)-dimethylarginine dimethylaminohydrolase and phosphoglycerate kinase 1 were decreased after cold perfusion. Perfusion with Doxy led to an increase in their levels. CONCLUSIONS: This study revealed specific metabolic enzymes involved in preservation injury and in the mechanism whereby Doxy protects the kidney against injury during cold perfusion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".