Application of flow cytometry‐based genotyping for rapid detection of hemoglobin variants
Bibliographic record
Abstract
The hemoglobinopathies represent a genetically heterogeneous group of disorders. Clinically important hemoglobin variants have been increasingly reported in the USA. Consequently, rapid and accurate testing methods are needed to address the growing diagnostic challenges of identifying these variants. To evaluate the utility of the Luminex LabMAP system for hemoglobinopathy testing, we adapted single base primer extension (SBPE) to this platform to detect 11 clinically important hemoglobin variants. Clinical samples from 11 individuals were tested for five beta-globin mutations (C-Harlem, D-Iran, Fannin-Lubbock and Hope) and six alpha-globin mutations (J-Toronto, Hasharon, G-Philadelphia, G-Norfolk, Constant-Spring and Quong-Sze). Two separate multiplexed SBPE assays were developed. Biotinylated amplification products were hybridized to fluorescent microspheres tagged with allele-specific capture probes and analyzed by flow cytometry on the Luminex100 instrument. The median fluorescent intensity (MFI) ranged from 1255 to 7478 fluorescence units (FU) and from 282 to 2609 FU above background for all positive beta-globin and alpha-globin alleles, respectively. Using the highest background MFI + 3 SD as a conservative threshold, MFI values uniformly discriminated wild type from mutant alleles, and genotypes were correctly identified in all samples tested. This pilot study demonstrates the potential application of the Luminex LabMAP genotyping platform to newborn screening for definitive hemoglobinopathy testing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".