Alpha-1-antitrypsin safely promotes rapid recovery of pigs after lung transplantation
Bibliographic record
Abstract
Primary graft dysfunction is an important complication requiring surveillance in the first 3 days after lung transplantation. Currently, there is no effective clinical therapy to prevent or treat primary graft dysfunction. Alpha-1-antitrypsin (A1AT) is a serine-protease inhibitor that inhibits neutrophil elastase and has been used for decades as a safe augmentation therapy for patients with A1AT deficiency. We have previously shown that administration of A1AT results in antiinflammatory and antiapoptotic effects that combat ischemia-reperfusion injury across cell culture, preclinical (rat and pig) nonsurvival lung transplant, and pig ex vivo lung perfusion models. 1 Gao W. Zhao J. Kim H. et al. Alpha1-antitrypsin inhibits ischemia reperfusion-induced lung injury by reducing inflammatory response and cell death. J Heart Lung Transplant. 2014; 33: 309-315https://doi.org/10.1016/j.healun.2013.10.031 Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar , 2 Iskender I. Sakamoto J. Nakajima D. et al. Human alpha1-antitrypsin improves early post-transplant lung function: pre-clinical studies in a pig lung transplant model. J Heart Lung Transplant. 2016; 35: 913-921https://doi.org/10.1016/j.healun.2016.03.006 Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar , 3 Lin H. Chen M. Tian F. et al. α1-Anti-trypsin improves function of porcine donor lungs during ex-vivo lung perfusion. J Heart Lung Transplant. 2018; 37: 656-666https://doi.org/10.1016/j.healun.2017.09.019 Abstract Full Text Full Text PDF PubMed Scopus (55) Google Scholar Before initiating a clinical trial, we further examined the potential clinical benefit and safety of A1AT in a pig single lung transplant 3-day survival model. This model was designed to replicate the clinical setting as closely as possible prior to proceeding to a clinical trial. 4 Mariscal A. Caldarone L. Tikkanen J. et al. Pig lung transplant survival model. Nat Protoc. 2018; 13: 1814-1828https://doi.org/10.1038/s41596-018-0019-4 Crossref PubMed Scopus (26) Google Scholar
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".