Novel intronic RHD variants identified in serologically D‐negative blood donors
Bibliographic record
Abstract
BACKGROUND: Blood group genotyping is used to predict RhD phenotype in transfusion and obstetric medicine. Prediction of antigen D is based on molecular techniques which targets most common RHD-specific polymorphism. However, inactive RHD variants can suggest false-positive RhD phenotype. Their types and frequencies vary among ethnicities. Our study aimed to identify RHD variants among Moroccan blood donors who are serologically D negative. STUDY DESIGN AND METHODS: DNA from 53 blood donors who are serologically D negative RhC and/or RhE positive were screened for RHD exon 10 by PCR-SSP. RHD-positive samples were further tested by multiplex PCR covering exons 3, 4, 5, 6, 7 and 9 and then sequenced by targeted next-generation sequencing method. Mutations' impact on mRNA splicing was predicted using alamut software version 2·0. RESULTS: PCR-SSP revealed 9 of 53 (16·9%) RHD-positive samples. Five of nine samples were positive for all tested exons, two of nine were positive for exon 9, and two of nine were undetermined. Sequencing revealed four novel RHD variants based on six mutations in introns 1, 3, 5 and 6. In silico analysis revealed aberrant splicing of three mutations (RHD c.487-1024delG, RHD c.487-256T>G and RHD c.940-187_940-188del), while three other mutations (RHD c.149-682C>A, RHD c.802-37delA and RHD c.939 + 1151dup) had no effect on splicing compared to wild type. CONCLUSIONS: All identified RHD variants contain at least one mutation that probably affects splicing to generate D-negative phenotype. Hence, ethnic RhD antigen background must be considered when developing transfusion and obstetric strategies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".