High‐throughput multiplex single‐nucleotide polymorphism analysis for red cell and platelet antigen genotypes
Bibliographic record
Abstract
BACKGROUND: Transfusion recipients who become alloimmunized to red cell or platelet (PLT) antigens require antigen-negative blood to limit adverse transfusion reactions. Blood collection facilities use regulated and unregulated antibodies to phenotype blood, the cost of which can be prohibitive depending on the antisera and demand. An alternative strategy is to screen blood for these antigens with genomic DNA and the associated single-nucleotide polymorphisms (SNPs). STUDY DESIGN AND METHODS: A multiplex polymerase chain reaction (PCR)-oligonucleotide extension assay was developed with genomic DNA and a SNP genotyping platform (GenomeLab SNPstream, Beckman Coulter) to identify SNPs related to D, C/c, E, S/s, K/k, Kp(a/b), Fy(a/b), FY0 (-33 promoter silencing polymorphism), Jk(a/b), Di(a/b), and human PLT antigen (HPA)-1a/1b. A total of 372 samples were analyzed for 12 SNPs. The genotypes were compared to the blood group and PLT antigen phenotypes. RESULTS: Individual sample results varied from 98 to 100 percent for 11 of 12 SNPs. D was correctly identified in 292 of 296 (98.6%) D+ donors. The RHCE exon 5 E/e SNP analysis had the lowest concordance (89.5%). Thirty-three R(1)R(1) and 1 r"r were correctly identified. PCR-restriction fragment length polymorphism (RFLP) on selected samples confirmed the presence of the FY0 silencing polymorphism in nine donors. Homozygous HPA-1b/1b was identified in four donors, which was confirmed by PCR-RFLP (n = 4) and anti-HPA-1a serology (n = 2). The two HPA-1a-negative donors were recruited into the plateletpheresis program. CONCLUSION: The platform has the capacity to genotype thousands of samples per day. The suite of SNPs provides genotype data for all blood donors within 36 hours of the start of testing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".