<scp>PCR</scp>‐free blood group genotyping using a nanobiosensor
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The last two decades have seen major developments in genotyping assays to facilitate the procurement of red blood cell units to alloimmunized patients. To make genotyping faster, simpler and less costly, a nanotechnology approach based on metal/silica fluorescent nanoparticles and a polymer-based hybridization optical transducer was designed. The objectives of this study were (1) to verify whether this nanobiosensor has the ability to discriminate single nucleotide polymorphisms in non-amplified genomic DNA and (2) to establish whether the signal generated by the nanobiosensor is sufficiently intense to be detected by standard flow cytometry. MATERIALS AND METHODS: Silver-core silica-shell fluorescent nanoparticles (Ag@SiO₂) were prepared, and amine-modified DNA probes were grafted to their surface. A cationic conjugated polymer was electrostatically bound to the surface probes to become optically active upon hybridization with a target. Two nanobiosensor formulations specific to DO*01 and DO*02 alleles were prepared. DNA was extracted from whole blood and mixed with the nanobiosensor for hybridization. The nanobiosensor fluorescence was measured by flow cytometry. RESULTS: Nine volunteers were typed for Dombrock blood group antigens DO*01 and DO*02. A statistically significant increase in the optical transduction signal was observed for sequence-specific samples. All nine genotypes were correctly identified when compared to standardized PCR assays. CONCLUSION: The nanobiosensor provides rapid and simple genotyping of blood group antigens from unamplified genomic DNA and can be measured using standard flow cytometers. This PCR-free approach could be applied to any known genetic polymorphism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".