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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".