A method for translational rat ex vivo lung perfusion experimentation
Bibliographic record
Abstract
The application of ex vivo lung perfusion (EVLP) has significantly increased the successful clinical use of marginal donor lungs. While large animal EVLP models exist to test new strategies to improve organ repair, there is currently no rat EVLP model capable of maintaining long-term lung viability. Here, we describe a new rat EVLP model that addresses this need, while enabling the study of lung injury due to cold ischemic time (CIT). The technique involves perfusing and ventilating male Lewis rat donor lungs for 4 h before transplanting the left lung into a recipient rat and then evaluating lung function 2 h after reperfusion. To test injury within this model, lungs were divided into groups and exposed to different CITs (i.e., 20 min, 6 h, 12 h, 18 h and 24 h). Experiments involving the 24-h-CIT group were prematurely terminated due to the development of severe edema. For the other groups, no differences in the ratio of arterial oxygen partial pressure to fractional inspired oxygen ([Formula: see text]/[Formula: see text]) were observed during EVLP; however, lung compliance decreased over time in the 18-h group ( P = 0.012) and the [Formula: see text]/[Formula: see text] of the blood from the left pulmonary vein 2 h after transplantation was lower compared with 20-min-CIT group ( P = 0.0062). This new model maintained stable lung function during 4-h EVLP and after transplantation when exposed to up to 12 h of CIT.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".