Single tube allele specific PCR: a low cost technique for molecular screening of sickle cell anaemia in Nigeria
Bibliographic record
Abstract
BACKGROUND: Sickle cell anaemia (SCA) is a hereditary blood disorder caused by a single mutation in the haemoglobin gene. The disease burden of SCA is highest in Nigeria. The allele specific polymerase chain reaction (ASPCR) method is applicable for the direct detection of known single nucleotide polymorphisms (SNPs). OBJECTIVE: To investigate the use of the single tube ASPCR as an accurate and affordable method for SCA screening in Nigeria. METHODS: DNA was extracted from study subjects with normal haemoglobin, HbAA (20), sickle cell anaemia, HbSS (20) and carriers, HbAS (1). Haemoglobin was genotyped by ASPCR using two primer sets that amplifies the wildtype and mutant haemoglobins in each sample. Amplicon sizes were analyzed by gel electrophoresis. RESULTS: Amplicons were visible after electrophoresis at regions 517 base pair (bp) for HbA and 267 bp for HbS. ASPCR correctly and unambiguously detected the presence or absence of haemoglobins A and S from all samples collected, demonstrating its accuracy and precision for the screening of SCA. CONCLUSION: This study validates ASPCR as an effective, low cost approach for the clinical screening of SCA in Nigeria. ASPCR is also applicable for other genetic diseases, paternity testing, and forensics where more expensive fluorescence-based approaches are not obtainable.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".