Dynamic Changes in Apoptotic and Necrotic Cell Death Correlate with Severity of Ischemia–Reperfusion Injury in Lung Transplantation
Bibliographic record
Abstract
Ischemia-reperfusion (IR) injury is a major cause of organ dysfunction following lung transplantation. We have recently described increased apoptosis in transplanted human lungs after graft reperfusion. However, a direct correlation between ischemic time, cell death, and posttransplant lung function has not yet been demonstrated. We hypothesized that an increased ischemic period would lead to an increase in cell death, and that the degree and type of cell death would correlate with lung function. To investigate this, we preserved rat lungs at 4 degrees C for 20 min and 6, 12, 18, and 24 h, and then transplanted the lungs and reperfused them for 2 h. Cell viability was determined with a triple staining technique combining trypan blue, terminal deoxynucleotidyl transferase-uridine nucleotide end-labeling, and propidium iodide nuclear staining. Percentages of apoptotic and necrotic cells were calculated from total cell numbers. Following 20 min and 6 and 12 h of cold preservation, less than 2% of graft cells were dead, whereas after 18 and 24 h of cold preservation, 11% and 27% of cells were dead (p < 0.05), the majority of which were necrotic. After transplantation and reperfusion, the mode of cell death changed significantly. In the 6- and 12-h groups, approximately 30% of cells were apoptotic and < 2% were necrotic, whereas in the 18- and 24-h groups, 21% and 29% of cells, respectively, were necrotic and less than 1% were apoptotic. Lung function (Pa(O(2))) decreased significantly (p < 0.05) with increasing preservation time. The percentage of necrotic cells was inversely correlated with posttransplant graft function (p < 0.0001). The study demonstrates a significant association among cold preservation time, extent and mode of cell death, and posttransplant lung function, and suggests new potential strategies to prevent and treat IR injury.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".