Pre-Implantation Multiple Cytokine mRNA Expression Analysis of Donor Lung Grafts Predicts Survival After Lung Transplantation in Humans
Bibliographic record
Abstract
While current donor selection with clinical findings is generally effective, the imprecise nature of the assessment forces clinicians to remain on the conservative side. A reliable biological marker would assist donor selection and would improve donor organ utilization. We collected biopsies from 169 donor lungs before implantation. Expression levels of IL-6, IL-8, IL-10, TNF-alpha, IFN-gamma and IL-1beta were measured by quantitative real-time RT-PCR (qRT-PCR). Seventeen cases died within 30 days after transplantation. No donor factor was significantly associated with 30-day mortality. Univariate analysis of the 84 cases for development of the prediction model showed that IL-6, IL-8, TNF-alpha and IL-1beta were risk factors for mortality and IL-10 and IFN-gamma were protective factors. We analyzed the cytokine expression ratios of risk to protective cytokines. A stepwise logistic regression for 30-day mortality demonstrated that a model containing the ratio of IL-6/IL-10 was the most predictive (p = 0.0013). When applied to the remaining 85 cases for validation, the test of model fit was significant (p = 0.039). Using the cytokine ratio, we were able to define three risk groups with striking differences in survival (p = 0.0003). Multi-cytokine analysis of the donor lung graft with qRT-PCR shows significant promise as a strategy to biologically evaluate the donor lung prior to implantation.
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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".