Genotyping for platelet‐specific antigens: techniques for the detection of single nucleotide polymorphisms
Bibliographic record
Abstract
Accurate typing of patients for platelet-specific (human platelet) antigens (HPA) is required in several different clinical situations, and blood services need to maintain panels of HPA-typed apheresis platelet donors and whole-blood donors to support HPA alloimmunized patients. Six clinically relevant HPA alloantigen systems have been described and, in addition, a significant number of HPA alloantigens with a highly skewed allele frequency or of very low immunogenicity have been reported. Certain well-characterized biallelic systems such as Gov have not as yet been included in the HPA nomenclature but are included in this review. Biochemical studies have identified the platelet membrane proteins on which the HPA antigens are localized. Cloning of the genes encoding these proteins and the realization that there is adequate mRNA in fresh platelets has led to identification of the molecular basis of HPA antigens over the last decade. All but one of the biallelic platelet-specific alloantigen systems are based on a single nucleotide polymorphism in the DNA sequence, corresponding to a single amino acid substitution in the encoded primary protein sequence. The discovery of the genetic basis of the alloantigens has allowed the development of polymerase chain reaction-based techniques for HPA genotyping using genomic DNA. The genetic basis of the HPA alloantigens, the most commonly used genome typing techniques and their pitfalls, and future developments, are discussed in this review.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".