Ex vivo lung perfusion-to-lung transplant rat survival model with reproducible development of acute lung injury and graft rejection
Bibliographic record
Abstract
Background: Ex vivo lung perfusion (EVLP) has been clinically applied as a lung preservation and assessment tool prior to lung transplantation (LTx) and is evolving to become a platform to deliver cellular and gene therapies or inactivate pathogens. Here we aimed to investigate the utility of our recently reported rat EVLP-to-LTx model as the smallest ever experimental survival model of EVLP-to-LTx and to compare late graft endpoints between strain combinations. Methods: We tested three strains as normothermic EVLP donors: Fisher 344 (F344), Lewis (LEW), and Wistar Kyoto (WKy) rats. Then we tested three strain combinations of EVLP-to-LTx (F344-to-WKy, F344-to-LEW, and LEW-to-LEW) to compare histologic and radiologic changes. Results: F344 and LEW, but not WKy rat lungs, tolerated 4 hours of normothermic EVLP. F344-to-WKy EVLP-to-LTx developed significant histologic (as measured by acute lung injury score, ISHLT A and B grade rejection score) and radiologic (volume and mean Hounsfield units of aerated lung graft analyzed by computed tomography at day 7 after EVLP-to-LTx) changes in the lung allograft. In this strain combination, progressive deterioration with time was noted up to day 28, while F344-to-LEW grafts exhibited only mild injuries similar to LEW-to-LEW. In addition, flow cytometric analyses of F344-to-WKy EVLP-to-LTx revealed a sharp rise in activation marker expression in lung graft T cells beginning at day 3. Conclusions: Our F344-to-WKy EVLP-to-LTx model generates reproducible and clinically relevant histological, radiological, and immunological results similar to those seen in humans. The model is therefore well suited to experimental EVLP studies with long-term follow-up prior to moving to large animal and human studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".