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ASSESSMENT OF ABO-HISTOCOMPATIBILITY: MODERNIZING ABO ANTIBODY DETECTION TOOLS FOR USE IN TRANSPLANTATION

2020· article· en· W3082681380 on OpenAlexaff
Anne Halpin, Bruce Motyka, J. Pearcey, T. Ellis, E. Dijke, Todd L. Lowary, Chris W. Cairo, Morgan Sosniuk, Stephanie Maier, Simon Urschel, Lori J. West

Bibliographic record

VenueTransplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsABO blood group systemTransplantationImmunologyAgglutination (biology)AntibodyMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: ABO-incompatible (ABOi) transplantation expands the donor pool for infants awaiting heart transplantation; this is possible as naturally occurring ABO antibodies (Abs) are not present at birth but develop by age 6-12 months. ABOi kidney transplantation can also be undertaken after Ab removal strategies, however unpredictability of risk assessment remains. The current ABO-Ab detection method using red cell agglutination is limited by lack of ABO-subtype specificity, imprecise ABO-Ab isotype differentiation, and poor reproducibility. We previously developed an ABO-glycan microarray to address these limitations. The field of histocompatibility has greatly advanced the accurate characterization of HLA Abs using bead-based tools; our aim here was to create a similar solid phase bead assay for ABO-Ab analysis. Materials and Methods: ABO A- and B-subtype antigens (I,II,III,IV,V,VI) were coupled to Luminex beads and quantified using monoclonal ABO-Abs. Bovine serum albumin was coupled as a negative control bead. IgG and IgM isotypes with specificities for ABO A- and B-subtypes were measured and compared in healthy adults (n=28) and pediatric patients (n=16) by mean fluorescence intensity (MFI). Samples were also tested on our glycan array and by red cell agglutination. (Overview of methods in Figure 1). Results: ABO-A and -B subtype-specific Abs were detected with high variability in MFI values amongst subjects. IgG and IgM anti-A and/or anti-B Abs were detectable in non-AB subjects, although MFIs were low in some as shown for anti-A in Figure 2. Anti-A and -B IgM MFIs were similar across individuals of blood group O, compared to B and A subjects (respectively). However, IgG MFIs were higher in O individuals than in A or B subjects. Importantly, the quantity of IgM antibodies did not predict IgG (Figure 3). IgM Ab alone did not predict RBC agglutination titre, as some subjects had mostly IgG ABO Abs not effectively detected by agglutination. Discussion: Initial results of our Luminex ABO-Ab assay are promising for eventual clinical laboratory development. Histocompatibility laboratories are well-positioned to support this testing as equipment, expertise, and patient samples are already in use. The specificity of this assay will allow precise assessment of ABO-Abs to antigen subtypes, known to be expressed differently in vascular endothelium than red cells. The ability to measure IgM and IgG ABO-Abs makes it possible to evaluate the role of each in potential allograft damage; isotype discrimination may be particularly relevant after plasmapheresis, which more efficiently removes IgM than IgG. Conclusion: Accurate immune risk assessment in ABOi transplantation relies on well-defined characterization of ABO Abs; improved ABO-Ab detection tools are required. This bead-based assay has the potential to serve as a powerful new tool for precise assessment of risk and for managing care of ABOi transplant patients.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.119
GPT teacher head0.401
Teacher spread0.282 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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