Integration of combined heteroduplex/restriction fragment length polymorphism analysis on an electrophoresis microchip for the detection of hereditary haemochromatosis
Bibliographic record
Abstract
This work describes an integrated method of enzymatic digestion, heteroduplex analysis (HA) and electrophoretic sizing on a microfluidic chip. HA techniques based on microchip electrophoresis are capable of the high sensitivity detection of subtle mutations such as single nucleotide polymorphisms (SNPs) but are not readily able to detect homozygous mutant genotypes. Such homozygous conditions are commonly encountered with the gene implicated in hereditary haemochromatosis, HFE. We employed the restriction fragment length polymorphism (RFLP) method of mutation detection to complement the HA method in a rapid novel on-chip procedure that separated digested PCR fragments to reliably determine the presence or absence of the most important mutations associated with haemochromatosis. This method was able to distinguish the homozygous mutant, heterozygous and homozygous wildtype genotypes. The mutations investigated here (C282Y, H63D and S65C) are often the mutation targets used in the genetic testing for haemochromatosis. This method provides the extremely specific digestion methods needed for the analysis of the known and relatively common mutations that have a significant probability of occurring in a homozygous form. However, the high sensitivity of the HA method is useful in detecting other mutations of lesser likelihood which, by virtue of their rarity, are likely to be present only in a heterozygous form. Although the conventional methods of analysing these mutations require as much as a day to perform, this microchip method, even without robotics or multiplexed operation, can be performed in about 10 min per sample.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".