<scp>SARA</scp>: a “new” low‐frequency<scp>MNS</scp>antigen (<scp>MNS47</scp>) provides further evidence of the extreme diversity of the<scp>MNS</scp>blood group system
Bibliographic record
Abstract
BACKGROUND: Until recently, SARAH (SARA) was a low-frequency antigen within the 700 series (700.052). SARA was discovered in Australia and subsequently described in Canada where anti-SARA was implicated in severe hemolytic disease of the fetus and newborn (HDFN). This study investigated whether SARA could be recategorized into an existing, or novel, blood group system. STUDY DESIGN AND METHODS: Serologically typed Australian SARA family members (n = 9) were exome sequenced followed by bioinformatics analysis. Sanger sequencing of Exon 3 of GYPA of Australian (n = 9) and Canadian (n = 9) family members was then performed, as were peptide inhibition studies. RESULTS: Exome sequencing identified 499,329 single-nucleotide variants (SNVs) within the nine individuals. Filtering excluded SNVs with an NCBI dbSNP ID (n = 482,177) and non-protein coding SNVs (n = 14,008); for the remaining 3144 SNVs, only one, c.240G>T of GYPA encoding p.Arg80Ser, was present in all six SARA-positive individuals. Sanger sequencing confirmed the presence of c.240G>T in the Australian SARA-positive individuals and demonstrated the same genetic basis in the Canadian SARA family. For a peptide representing the SARA sequence, inhibition of anti-SARA against SARA-positive cells was 84.6% at a concentration of 1.0 mg/mL. CONCLUSION: We provide evidence that the SARA antigen is encoded by a SNV on GYPA and SARA has been reassigned to the MNS blood group system, now MNS47. This discovery provides a basis for application of genetic approaches in SARA typing when clinically indicated, for example, in HDFN.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".