Detection of human platelet antigen‐1a alloantibodies in cases of fetomaternal alloimmune thrombocytopenia using recombinant β3 integrin fragments coupled to fluorescently labeled beads
Bibliographic record
Abstract
BACKGROUND: Testing for alloantibodies against human platelet antigens (HPAs) is essential for the clinical diagnosis of fetomaternal alloimmune thrombocytopenia (FMAIT), posttransfusion purpura, and platelet (PLT) refractoriness. Most of the methods currently used for HPA alloantibody detection rely on the availability of panels of HPA-typed PLTs and some rely on validated monoclonal antibodies (MoAbs) against the PLT glycoproteins. Recombinant β3 integrins displaying the HPA-1a (rHPA-1a) or HPA-1b (rHPA-1b) epitopes have been produced as an alternative source of antigen. The suitability of these integrin fragments was evaluated for the development of an HPA-1a alloantibody screening assay, using Luminex xMAP technology. STUDY DESIGN AND METHODS: A 3-plex bead assay was developed by coupling biotinylated rHPA-1a, rHPA-1b, and recombinant glycoprotein VI to LumAvidin microspheres. Forty patient samples referred for FMAIT diagnostic testing, which were previously screened by the MoAb-specific immobilization of PLT antigens (MAIPA) assay, were used to assess the assay. RESULTS: The rHPA-1a- and rHPA-1b-coupled beads were able to detect HPA-1a and HPA-1b alloantibodies in all patient samples tested that were previously confirmed to contain HPA-1-specific antibodies. Furthermore, HLA Class I antibodies did not cross-react with the coupled beads. CONCLUSION: The 3-plex bead assay can be used to detect HPA-1a antibodies with sufficient specificity and sensitivity for use in the clinical setting of FMAIT. The development of other recombinant integrin fragments with the use of Luminex xMAP technology may assist in providing more rapid HPA antibody detection, enabling prompt diagnosis of alloimmune PLT disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".