Circulating Cell Death Biomarkers May Predict Survival in Human Lung Transplantation
Bibliographic record
Abstract
RATIONALE: Immediate graft performance after lung transplantation is associated with short- and long-term clinical outcomes. However, the biologic mechanism that determines outcomes is not fully understood. OBJECTIVES: To investigate the impact of cell death signals at 24 and 48 hours after lung transplantation on short- and long-term clinical outcomes. METHODS: Plasma samples were collected pretransplantation and at 24 and 48 hours after transplant from 60 bilateral lung transplant recipients. Ten patients had primary graft dysfunction (PGD) grade 3 (PaO2/FiO2 ratio <200 or on extracorporeal membrane oxygenation support) at 72 hours after transplant (PGD group). The remaining 50 patients were defined as the control group. Levels of plasma M30 (signifying epithelial apoptosis), M65 (signifying epithelial apoptosis plus necrosis), and high-mobility group box 1 protein (HMGB-1; signifying necrosis of all cell types) were measured by ELISA and correlated with clinical outcomes. Survival analyses were performed using Kaplan-Meier curves and Cox proportional hazards regression. Prediction accuracy of markers was assessed by calculated area under the curve of receiver operating characteristic graph. MEASUREMENTS AND MAIN RESULTS: The PGD group had significantly higher M30 and M65 levels at 24 and 48 hours after transplant compared with the control group. There was no significant difference in HMGB-1. Area under the curve for 1-year survival was 0.86, 0.93, and 0.51 for M30, M65, and HMGB-1 at 48 hours, respectively. Survival analysis showed that higher M30 and M65 levels at 24 and 48 hours were significantly associated with worse survival. M65 at 48 hours remained significant even after adjustment for PGD. HMGB-1 was not significantly associated with survival. CONCLUSIONS: Recipient plasma concentration of epithelial cell death markers (M30, M65) after lung transplantation is negatively correlated with early graft performance and long-term survival.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".