L-Alanyl-L-Glutamine Alleviated Ischemia-Reperfusion Injury and Primary Graft Dysfunction in Rat Lung Transplants
Bibliographic record
Abstract
BACKGROUND: Concern of ischemia-reperfusion injury reduces utilization of donor lungs. We hypothesized adding L-alanyl-L-glutamine (L-AG) to preservation solution may protect donor lungs from ischemia-reperfusion injury through its multiple cytoprotective effects. METHODS: A lung transplantation cell culture model was used on human lung epithelial cells and pulmonary microvascular endothelial cells, and the effects of adding different concentrations of L-AG on basic cellular function were tested. Rat donor lungs were preserved at 4 °C with 8 mmol/L L-AG for 12 h followed by 4 h reperfusion or monitored for 3 d. Lung function, lung histology, inflammation, and cell death biomarker were tested. Computerized tomography scan was used and metabolomic analysis was performed on lung tissues. RESULTS: Cold preservation with L-AG improved cell viability and inhibited apoptosis in cell culture. Rat donor lungs treated with L-AG during cold storage showed decreased peak airway pressure, higher dynamic compliance and oxygenation ability, reduced lung injury, apoptosis, and oxidative stress during reperfusion. L-AG treatment significantly changed 130 metabolites during reperfusion, with enhanced amino acid biosynthesis and tricarboxylic acid cycle. Furthermore, cold storage with L-AG decreased primary graft dysfunction grade, improved oxygenation, reduced pulmonary atelectasis, sign of infection, and pneumothorax in a rat left lung transplant 3-d survival model. CONCLUSIONS: Adding L-AG to cold preservation solution reduced lung injury and alleviated primary graft dysfunction by inhibiting inflammation, oxidative stress, and cell death with modified metabolic activities.
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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 | 0.000 |
| 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) | 0.001 | 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".