Novel proof of concept assay for rapid, simplified non-invasive fetal RhD screening
Bibliographic record
Abstract
The RhD antigen is a key component of the Rh blood group system and is highly immunogenic, posing risks for RhD-negative pregnant women. While anti-D prophylaxis prevents maternal alloimmunization, concerns regarding availability, overuse, and universal effectiveness persist. Recent advances in non-invasive fetal RHD genotyping have shown potential to reduce unnecessary interventions. This study presents a new non-invasive fetal RhD screening method using cell-free DNA (cfDNA) and dry chemistry quantitative polymerase chain reaction (qPCR) with fluorescence measurements of three RHD exons, aimed at enhancing the accuracy and efficiency of RHD genotyping and improving clinical decision-making. We evaluated this new method using cfDNA extracted from plasma samples of 28 RhD-negative women carrying singleton pregnancies. The 20µL cfDNA samples correctly identified all 18 RhD-positive pregnancies and 9/10 RhD-negative pregnancy samples. Our study provides proof of concept that the cfDNA and dry chemistry qPCR method for fetal RHD genotyping is feasible, as it achieved 100% sensitivity. Despite the small sample size, these findings support the use of RHD genotyping as a precise and effective alternative or complement to anti-D prophylaxis, offering improved risk management and care for pregnant women worldwide.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".