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Record W4409385506 · doi:10.1016/j.ajt.2025.04.007

Antigen quantity is responsible for the discrepancy between phenotype and single antigen beads for the detection of human leukocyte antigen DQ antibody: Potential clinical implications

2025· article· en· W4409385506 on OpenAlexaff
Neng Jen Remi Shih, Thoa Nong, Robert A. Bray, Cathi Murphey, Ina Skaljic, Howard M. Gebel, Mayra Lopez‐Cepero, Peter Nickerson, Jar-How Lee

Bibliographic record

VenueAmerican Journal of Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHuman leukocyte antigenAntigenPhenotypeMedicineImmunologyAntibodyHistocompatibility TestingGeneticsBiologyGene

Abstract

fetched live from OpenAlex

De novo donor-specific human leukocyte antigen DQ antibodies detected by single antigen beads (SAB) are significantly associated with chronic antibody-mediated rejection and lower overall graft survival. However, some DQ antibodies identified by SAB cannot be confirmed by phenotype antigen-bearing class II bead assays, raising concerns about the validity of SAB data. The inability to detect these antibodies on phenotype antigen beads could be due to a lower quantity of DQ antigens present on the surface of the beads compared to DR antigens. In this study, we demonstrate that DQ-enriched phenotype antigens exhibit the same reactivity with DQ antibodies detected by SAB, confirming the hypothesis that it is antigen quantity and not structural differences that account for discrepancies in human leukocyte antigen DQ antibody detection between phenotype antigen beads and SAB. In addition, we show that the expression of individual DQ antigens in heterozygous cells can vary significantly, further confounding correlation studies. Therefore, the common clinical practice of using the phenotype antigen beads as a screening assay, reflexing to SAB testing only when positive, may inadvertently fail to detect DQ-specific antibodies. Such errors could impact organ acceptance practices, immunosuppression treatment decisions, and/or the need for additional diagnostic testing to rule out antibody-mediated rejection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.062
GPT teacher head0.436
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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