Metabolic Profile of Ex Vivo Lung Perfusate Yields Biomarkers for Lung Transplant Outcomes
Bibliographic record
Abstract
OBJECTIVE: To identify potential biomarkers during ex vivo lung perfusion (EVLP) using metabolomics approach. SUMMARY BACKGROUND DATA: EVLP increases the number of usable donor lungs for lung transplantation (LTx) by physiologic assessment of explanted marginal lungs. The underlying paradigm of EVLP is the normothermic perfusion of cadaveric lungs previously flushed and stored in hypothermic preservation fluid, which allows the resumption of active cellular metabolism and respiratory function. Metabolomics of EVLP perfusate may identify metabolic profiles of donor lungs associated with early LTx outcomes. METHODS: EVLP perfusate taken at 1and 4 hperfusion were collected from 50 clinical EVLP cases, and submitted to untargeted metabolic profiling with mass spectrometry. The findings were correlated with early LTx outcomes. RESULTS: Following EVLP, 7 cases were declined for LTx. In the remaining transplanted cases, 9 cases developed primary graft dysfunction (PGD) 3. For the metabolic profile at EVLP-1h, a logistic regression model based on palmitoyl-sphingomyelin, 5-aminovalerate, and decanoylcarnitine yielded a receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.987 in differentiating PGD 3 from Non-PGD 3 outcomes. For the metabolic profile at EVLP-4h, a logistic regression model based on N2-methylguanosine, 5-aminovalerate, oleamide, and decanoylcarnitine yielded a ROC curve with AUC 0.985 in differentiating PGD 3 from non-PGD 3 outcomes. CONCLUSIONS: Metabolomics of EVLP perfusate revealed a small panel of metabolites highly correlated with early LTx outcomes, and may be potential biomarkers that can improve selection of marginal lungs on EVLP. Further validation studies are needed to confirm these findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".