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Anti-RhD Mediates Loss of RhD Antigen Following Anti-RhD Infusion

2015· article· en· W2532453942 on OpenAlexaff
Harold C. Sullivan, Connie M. Arthur, Seema R. Patel, Jeanne E. Hendrickson, Alan H. Lazarus, Sean R. Stowell

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsAntigenAntibodyImmunologyAutoantibodyComplement systemIsoantibodiesAutoimmune hemolytic anemiaRed blood cellHemolysisMedicine

Abstract

fetched live from OpenAlex

Abstract Background: Previous studies suggest that antibody engagement of red blood cells (RBCs) can result in the actual loss of detectable target antigen in the setting of autoimmune hemolytic anemia. However, many of these situations appear to represent complement mediated-masking of the target antigen. Recent studies suggest that complement-independent antigen-loss can occur in a variety of murine models. However, whether a similar form of RBC antigen loss can occur independent of complement in humans remains unknown. As previous studies suggest that anti-RhD fails to induce significant complement activation following engagement of RhD-positive RBCs, we evaluated the potential impact of anti-RhD antibody injection on RhD antigen levels in a RhD-positive patient being treated for idiopathic thrombocytopenic purpura. Methods: Pre- and post-anti-RhD infusion samples were obtained from the hospital blood bank. Direct antiglobulin tests (DATs) were performed and assessed via flow cytometry on pre- and post-infusion samples. RBCs from pre- and post-infusion were also treated with Warm Autoantibody Removal Medium (WARM; ImmucorGamma, Norcross, GA), a sulphydryl/enzyme reagent based on the ZZAP method, in order to remove bound antibody. Post-WARM treated cells from pre- and post-anti-RhD infusion samples were then used to perform DATs. The presence of RhD antigen was assessed in pre- and post-infusion samples with in vitro anti-RhD, from the same lot number used to treat the patient, followed by anti-IgG and anti-complement as the secondary antibody. To account for the potential impact of bound antibody on antigen detection, antigen levels were also assessed after anti-RhD removal with the WARM method. Finally, in order to determine whether antigen loss was RhD specific, cellano (k) antigen detection was tested using anti-k antibody both pre- and post-WARM treatment. Results: As predicted, the pre-anti-RhD infusion DAT was negative, while the post-anti-RhD injection DAT was positive (MFI 25). Post-WARM treatment, pre-and post-RhD infusion DATs showed minimal reactivity (MFI 3 and 5, respectively with background MFI of 2.7). Reduced RhD antigen levels were observed in post-anti-RhD infusion samples when compared to pre-infusion samples, while no complement could be detected in either pre- or post-anti-RhD infusion samples. The detection of antigen loss post-anti-RhD infusion was even more pronounced after RBCs were treated with WARM to remove previously bound anti-RhD antibody administered in vivo. In contrast, no difference in k antigen level could be detected pre- or post-anti-RhD infusion. As expected, post-WARM treatment, k antigen was no longer detectable pre- or post-anti-RhD infusion samples. Conclusion: These results provide an example of antigen loss in the setting of anti-RhD administration. Moreover, the anti-RhD effect on the RhD-antigen appears to be antigen specific, as the RhD immune globulin did not modulate the k antigen on the same cell. Taken together, these results suggest that anti-RhD can induce the loss of detectable antigen independent of complement and may therefore influence the rate and magnitude of RBC clearance in settings of anti-RhD infusion, incompatible RBC transfusion and autoimmune hemolytic anemia. Disclosures No relevant conflicts of interest to declare.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2015
Admission routes1
Has abstractyes

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