Cell Death in Human Lung Transplantation: Apoptosis Induction in Human Lungs During Ischemia and After Transplantation
Bibliographic record
Abstract
OBJECTIVE: To examine the presence and extent of apoptosis as well as the affected cell types in human lung tissue before, during, and after transplantation. SUMMARY BACKGROUND DATA: Apoptosis has been described in various human and animal models of ischemia-reperfusion injury, including heart, liver, and kidney, but not in lungs. Therefore, the presence of apoptosis and its role in human lungs after transplantation is not clear. METHODS: Lung tissue biopsies were obtained from 20 consecutive human lungs for transplantation after cold ischemic preservation (1-5 hours), after warm ischemia time (during implantation), and 30, 60, and 120 minutes after graft reperfusion. To detect and quantify apoptosis, fluorescent in situ end labeling of DNA fragments (TUNEL assay) was used. Electron microscopy was performed to verify the morphologic changes consistent with apoptosis and to identify the cell types, which were lost by apoptosis. RESULTS: Almost no evidence of apoptosis was found in specimens after immediate cold and warm ischemic periods. Significant increases in the numbers of cells undergoing apoptosis were observed after graft reperfusion in a time-dependent manner. The mean fraction of apoptotic cells at 30, 60, and 120 minutes after graft reperfusion were 16.6%, 22.1%, and 34.9% of total cells, respectively. Most of the apoptotic cells appeared to be alveolar type II pneumocytes, as confirmed by electron microscopy. CONCLUSIONS: Programmed cell death (apoptosis) appears to be a significant type of cell loss in human lungs after transplantation, and this may contribute to ischemia-reperfusion injury during the early phase of graft reperfusion. This cell loss might be responsible for severe organ dysfunction, which is seen in 20% of patients after lung transplantation. Therefore, this work is of importance to surgeons for the future development of interventions to prevent cell death in transplantation.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".