ABH-Glycan Microarray Characterizes ABO Subtype Antibodies: Fine Specificity of Immune Tolerance After ABO-Incompatible Transplantation
Bibliographic record
Abstract
Organ transplantation from ABO blood group-incompatible (ABOi) donors requires accurate detection, effective removal and subsequent surveillance of antidonor antibodies. Because ABH antigen subtypes are expressed differently in various cells and organs, measurement of antibodies specific for the antigen subtypes in the graft is essential. Erythrocyte agglutination, the century-old assay used clinically, does not discriminate subtype-specific ABO antibodies and provides limited information on antibody isotypes. We designed and created an ABO-glycan microarray and demonstrated the precise assessment of both the presence and, importantly, the absence of donor-specific antibodies in an international study of pediatric heart transplant patients. Specific IgM, IgG, and IgA isotype antibodies to nonself ABH subtypes were detected in control participants and recipients of ABO-compatible transplants. Conversely, in children who received ABOi transplants, antibodies specific for A subtype II and/or B subtype II antigens-the only ABH antigen subtypes expressed in heart tissue-were absent, demonstrating the fine specificity of B cell tolerance to donor/graft blood group antigens. In contrast to the hemagglutination assay, the ABO-glycan microarray allows detailed characterization of donor-specific antibodies necessary for effective transplant management, representing a major step forward in precise ABO antibody detection.
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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".