Increased Arginase Expression and Decreased Nitric Oxide in Pig Donor Lungs after Normothermic Ex Vivo Lung Perfusion
Bibliographic record
Abstract
An established pig lung transplantation model was used to study the effects of cold ischemia time, normothermic acellular ex vivo lung perfusion (EVLP) and reperfusion after lung transplantation on l-arginine/NO metabolism in lung tissue. Lung tissue homogenates were analyzed for NO metabolite (NOx) concentrations by chemiluminescent NO-analyzer technique, and l-arginine, l-ornithine, l-citrulline and asymmetric dimethylarginine (ADMA) quantified using liquid chromatography-mass spectrometry (LC-MS/MS). The expression of arginase and nitric oxide synthase (NOS) isoforms in lung was measured by real-time polymerase chain reaction. EVLP preservation resulted in a significant decrease in concentrations of NOx and l-citrulline, both products of NOS, at the end of EVLP and after reperfusion following transplantation, compared to control, respectively. The ratio of l-ornithine over l-citrulline, a marker of the balance between l-arginine metabolizing enzymes, was increased in the EVLP group prior to reperfusion. The expression of both arginase isoforms was increased from baseline 1 h post reperfusion in EVLP but not in the no-EVLP group. These data suggest that EVLP results in a shift of the l-arginine balance towards arginase, leading to NO deficiency in the lung. The arginase/NOS balance may, therefore, represent a therapeutic target to improve lung quality during EVLP and, subsequently, transplant outcomes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".