Ex vivo enzymatic treatment converts blood type A donor lungs into universal blood type lungs
Bibliographic record
Abstract
Donor organ allocation is dependent on ABO matching, restricting the opportunity for some patients to receive a life-saving transplant. The enzymes FpGalNAc deacetylase and FpGalactosaminidase, used in combination, have been described to effectively convert group A (ABO-A) red blood cells (RBCs) to group O (ABO-O). Here, we study the safety and preclinical efficacy of using these enzymes to remove A antigen (A-Ag) from human donor lungs using ex vivo lung perfusion (EVLP). First, the ability of these enzymes to remove A-Ag in organ perfusate solutions was examined on five human ABO-A1 RBC samples and three human aortae after static incubation. The enzymes removed greater than 99 and 90% A-Ag from RBCs and aortae, respectively, at concentrations as low as 1 μg/ml. Eight ABO-A1 human lungs were then treated by EVLP. Baseline analyses of A-Ag in lungs revealed expression predominantly in the endothelial and epithelial cells. EVLP of lungs with enzyme-containing perfusate removed over 97% of endothelial A-Ag within 4 hours. No treatment-related acute lung toxicity was observed. An ABO-incompatible transplant was then simulated with an ex vivo model of antibody-mediated rejection using ABO-O plasma as the surrogate for the recipient circulation using three donor lungs. The treatment of donor lungs minimized antibody binding, complement deposition, and antibody-mediated injury as compared with control lungs. These results show that depletion of donor lung A-Ag can be achieved with EVLP treatment. This strategy has the potential to expand ABO-incompatible lung transplantation and lead to improvements in fairness of organ allocation.
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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) | 0.000 | 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) | 0.001 | 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".