Metabolomic studies reveal an organ-protective hibernation state in donor lungs preserved at 10 °C
Bibliographic record
Abstract
OBJECTIVE: Previous reports showed enhanced graft function in both healthy and injured porcine lungs after preservation at 10 °C. The objective of the study is to elucidate the mechanism of lung protection by 10 °C and identify potential therapeutic targets to improve organ preservation. METHODS: Metabolomics data were analyzed from healthy and injured porcine lungs that underwent extended hypothermic preservation on ice and at 10 °C. Tissue sampled before and after preservation were subjected to untargeted metabolic profiling. Principal component analysis was performed to test for the separability of the paired samples. Significantly changed metabolites between the 2 time points were identified and analyzed to determine the underlying metabolic pathways. The levels of respiratory activity of lung tissue at hypothermic temperatures were confirmed using high resolution respirometry. RESULTS: In both healthy and injured lungs (n = 5 per intervention), principal component analysis suggested minimal change in metabolites after ice preservation but significant change of metabolites after 10 °C preservation, which was associated with significantly improved lung function as assessed by ex vivo lung perfusion and lung transplantation. For healthy lungs, lipid energy pathway was found primarily active at 10 °C. For injured lungs, additional carbohydrate energy pathway and anti-ferroptosis pathways aiding organ repair were identified. These metabolic features are also key features involved in mammal hibernation. CONCLUSIONS: Untargeted metabolomics revealed a dynamic metabolic gradient for lungs stored at 10 °C. Elucidating the underlying mechanisms behind this pathway regulation may lead to strategies that will allow organs "hibernate" for days, potentially making organ banking a reality.
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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 | 0.000 |
| 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".