Multiplex bead immunoassay in ABO-A2-incompatible kidney transplantation
Bibliographic record
Abstract
Kidney transplantation from ABO-A2 donors into ABO-O and ABO-B recipients can alleviate inequitable transplant access created by ABO demographics. ABO-A2-incompatible (ABO-A2i) eligibility is determined by anti-A hemagglutination titers. However, titers do not distinguish antibodies specific for A-II glycans, the sole A-antigen subtype in vascular endothelium, from other anti-A antibodies. We examined whether reliance on anti-A titers unnecessarily limited ABO-A2i transplants for candidates with low anti-A-II levels. We created a single-antigen bead immunoassay for ABO antibodies, confirmed the specificity and reproducibility, and demonstrated the ability to detect anti-A and anti-B glycan subtype-specific antibodies in healthy control sera. We then measured subtype-specific anti-A antibodies in original sera from ABO-B and ABO-O candidates who had been previously evaluated for ABO-A2i eligibility. Anti-A-II levels in candidates who had been deemed ineligible (anti-A titers >4) were compared to eligible candidates (anti-A titers ≤4) who had subsequently received ABO-A2i kidneys. Of 141 candidates, 75 (53%) were ineligible; 66 (47%) were eligible and received ABO-A2 kidneys. Retesting original sera, 55% (41/75) of ineligible candidates had anti-A-II levels comparable to eligible candidates. Anti-A titers did not reflect anti-A-II levels. Our ABO antibody assay reproducibly measures graft-specific anti-A-II antibodies, providing information beyond anti-A titers that may increase transplant access for ABO-B and ABO-O candidates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".