Protein Expression Profiling Predicts Graft Performance in Clinical Ex Vivo Lung Perfusion
Bibliographic record
Abstract
In Brief Objectives: To study the impact of ex vivo lung perfusion (EVLP) on cytokines, chemokines, and growth factors and their correlation with graft performance either during perfusion or after transplantation. Background: EVLP is a modern technique that preserves lungs on normothermia in a metabolically active state. The identification of biomarkers during clinical EVLP can contribute to the safe expansion of the donor pool. Methods: High-risk brain death donors and donors after cardiac death underwent 4 to 6 hours EVLP. Using a multiplex magnetic bead array assay, we evaluated analytes in perfusate samples collected at 1 hour and 4 hours of EVLP. Donor lungs were divided into 3 groups: (I) Control: bilateral transplantation with good early outcome [absence of primary graft dysfunction– (PGD) grade 3]; (II) PGD3: bilateral transplantation with PGD grade 3 anytime within 72 hours; (III) Declined: lungs unsuitable for transplantation after EVLP. Results: Of 50 cases included in this study, 27 were in Control group, 7 in PGD3, and 16 in Declined. From a total of 51 analytes, 34 were measurable in perfusates. The best marker to differentiate declined lungs from control lungs was stem cell growth factor -β [P < 0.001, AUC (area under the curve) = 0.86] at 1 hour. The best markers to differentiate PGD3 cases from controls were interleukin-8 (P < 0.001, AUC = 0.93) and growth-regulated oncogene-α (P = 0.001, AUC = 0.89) at 4 hours of EVLP. Conclusions: Perfusate protein expression during EVLP can differentiate lungs with good outcome from lungs PGD3 after transplantation. These perfusate biomarkers can be potentially used for more precise donor lung selection improving the outcomes of transplantation. This is the first study to examine protein expression in perfusates of high-risk donor lungs submitted to clinical ex vivo lung perfusion. Perfusate markers were capable of predicting graft function after implantation. Our findings have a strong potential for clinical translation toward improving donor lung selection and transplantation 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 | 0.001 |
| 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 | 0.001 | 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".