Rapid and specific detection of clinically significant haemoglobinopathies using electrospray mass spectrometry–mass spectrometry
Bibliographic record
Abstract
Increasing demand for population screening for the haemoglobinopathies gives rise to a requirement for high throughput systems, which allow for cost effective, rapid, sensitive and specific screening of clinically significant haemoglobins. We have developed a practical and efficient approach using tryptic digestion and electrospray triple quadrupole mass spectrometry-mass spectrometry (MSMS) in multiple reaction monitoring acquisition mode for the identification of the clinically important haemoglobin variants, S, C, DPunjab, OArab, and E. A total of 200 blood samples, comprising 52 haemoglobin AA, 57 AS (sickle cell trait), 44 AC (C trait), 16 SC (SC disease), 14 SS (sickle cell disease), 10 AE (E trait), 2 ADPunjab (DPunjab trait) and 1 each of AOArab (OArab trait), CC (C disease), DPunjabDPunjab (DPunjab disease), OArabOArab (OArab disease), and EE (E disease), have been analysed in parallel with existing phenotype and molecular methods. All haemoglobin variants were correctly identified by MSMS, with no false positives or false negatives. The system detects both heterozygotes and homozygotes and has potential applications in neonatal and antenatal screening.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".