Ex vivo enzymatic treatment converts blood type A donor lungs into universal blood type lungs
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it