Lung Transplantation, Pulmonary Endothelial Inflammation, and Ex-Situ Lung Perfusion: A Review
Bibliographic record
Abstract
Lung transplantation (LTx) is the gold standard treatment for end-stage lung disease; however, waitlist mortality remains high due to a shortage of suitable donor lungs. Organ quality can be compromised by lung ischemic reperfusion injury (LIRI). LIRI causes pulmonary endothelial inflammation and may lead to primary graft dysfunction (PGD). PGD is a significant cause of morbidity and mortality post-LTx. Research into preservation strategies that decrease the risk of LIRI and PGD is needed, and ex-situ lung perfusion (ESLP) is the foremost technological advancement in this field. This review addresses three major topics in the field of LTx: first, we review the clinical manifestation of LIRI post-LTx; second, we discuss the pathophysiology of LIRI that leads to pulmonary endothelial inflammation and PGD; and third, we present the role of ESLP as a therapeutic vehicle to mitigate this physiologic insult, increase the rates of donor organ utilization, and improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".