Reperfusion‐Induced Gene Expression Profiles in Rat Lung Transplantation
Bibliographic record
Abstract
Ischemia-Reperfusion (I/R) injury after lung transplantation (LTx) can lead to significant morbidity and mortality in recipients. In an attempt to improve our understanding of molecular mechanisms of I/R injury, we examined the changes in gene expression levels in a rat lung transplant model using oligonucleotide microarrays. Lewis rat lung grafts were stored for 6 or 24 h followed by transplantation and reperfusion for 2 h. Lung tissues were taken before and after flushing the grafts, before implantation, and after 2 h of reperfusion. RNA samples were examined with Affymetrix rat microarray chips and RT-PCR was performed to validate significant changes in gene expression. Microarray analysis showed 404 genes that were up-regulated more than 2-fold after reperfusion compared to cold ischemic lungs, and 187 genes that were down-regulated. Using RT-PCR, we confirmed the response pattern of several specific gene transcripts from the microarray analysis. Among these, up-regulation in transcripts of transcription factors, adhesion molecules, pro-coagulant factors and pro-inflammatory cytokines were identified. The differential gene regulation during the I/R process can be considered as molecular signatures for the changes of cellular metabolism, functions and injury. Reperfusion-induced genes related to inflammatory response may contribute to graft dysfunction in LTx.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".