Impact of Human Donor Lung Gene Expression Profiles on Survival after Lung Transplantation: A Case-Control Study
Bibliographic record
Abstract
Primary graft dysfunction (PGD) continues to be a major cause of early death after lung transplantation. Moreover, there remains a lack of accurate pretransplant molecular markers for predicting PGD. To identify distinctive donor lung gene expression signatures associated with PGD, we profiled human donor lungs using microarray technology prior to implantation. The genomic profiles of 10 donor lung samples from patients who subsequently developed clinically defined severe PGD were compared with 16 case-matched donor lung samples from those who had a favorable outcome without PGD (development set, n = 26). Selected PCR validated predictive genes were tested by quantitative reverse transcription-polymerase chain reaction in an independent test set (n = 81). Our microarray analyses of the development set identified four significantly upregulated genes (ATP11B, FGFR2, EGLN1 and MCPH1) in the PGD samples. These genes were also significantly upregulated in donor samples of the test set of patients with poor outcomes when compared to those of patients with good outcomes after lung transplantation. This type of biological donor lung assessment shows significant promise for development of a more accurate diagnostic strategy to assess donor lungs 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".