RhD Specific Antibodies Are Not Detectable in HLA-DRB1<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mrow><mml:mn fontstyle="italic">1501</mml:mn></mml:mrow><mml:mrow><mml:mi mathvariant="normal">*</mml:mi></mml:mrow></mml:mrow></mml:math>Mice Challenged with Human RhD Positive Erythrocytes
Bibliographic record
Abstract
The ability to study the immune response to the RhD antigen in the prevention of hemolytic disease of the fetus and newborn has been hampered by the lack of a mouse model of RhD immunization. However, the ability of transgenic mice expressing human HLA DRB1(*)1501 to respond to immunization with purified RhD has allowed this question to be revisited. In this work we aimed at inducing anti-RhD antibodies by administering human RhD(+) RBCs to mice transgenic for the human HLA DRB1(*)1501 as well as to several standard inbred and outbred laboratory strains including C57BL/6, DBA1/J, CFW(SW), CD1(ICR), and NSA(CF-1). DRB1(*)1501 mice were additionally immunized with putative extracellular immunogenic RhD peptides. DRB1(*)1501 mice immunized with RhD(+) erythrocytes developed an erythrocyte-reactive antibody response. Antibodies specific for RhD could not however be detected by flow cytometry. Despite this, DRB1(*)1501 mice were capable of recognizing immunogenic sequences of Rh as injection with Rh peptides induced antibodies reactive with RhD sequences, consistent with the presence of B cell repertoires capable of recognizing RhD. We conclude that while HLA DRB1(*)1501 transgenic mice may have the capability of responding to immunogenic sequences within RhD, an immune response to human RBC expressing RhD is not directly observed.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".