Toward universal donor blood: Enzymatic conversion of A and B to O type
Bibliographic record
Abstract
Transfusion of blood, or more commonly red blood cells (RBCs), is integral to health care systems worldwide but requires careful matching of blood types to avoid serious adverse consequences. Of the four main blood types, A, B, AB, and O, only O can be given to any patient. This universal donor O-type blood is crucial for emergency situations where time or resources for typing are limited, so it is often in short supply. A and B blood differ from the O type in the presence of an additional sugar antigen (GalNAc and Gal, respectively) on the core H-antigen found on O-type RBCs. Thus, conversion of A, B, and AB RBCs to O-type RBCs should be achievable by removal of that sugar with an appropriate glycosidase. The first demonstration of a B-to-O conversion by Goldstein in 1982 required massive amounts of enzyme but enabled proof-of-principle transfusions without adverse effects in humans. New α-galactosidases and α- N -acetylgalactosaminidases were identified by screening bacterial libraries in 2007, allowing improved conversion of B and the first useful conversions of A-type RBCs, although under constrained conditions. In 2019, screening of a metagenomic library derived from the feces of an AB donor enabled discovery of a significantly more efficient two-enzyme system, involving a GalNAc deacetylase and a galactosaminidase, for A conversion. This promising system works well both in standard conditions and in whole blood. We discuss remaining challenges and opportunities for the use of such enzymes in blood conversion and organ transplantation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".