A DNA Hybridization Chip With Electrokinetically-Based Single Nucleotide Polymorphism (SNP) Discrimination
Bibliographic record
Abstract
Biosensors and more specifically biochips exploit the interactions between a target analyte and an immobilized biological recognition element to produce a measurable signal. Systems based on surface phase nucleic acid hybridization, such as modern microarrays, are particularly attractive due to the high degree of selectivity in the binding interactions. In this work an electrokinetically controlled poly(dimethylsiloxane) based DNA hybridization microfluidic chip is presented. The electrokinetic delivery technique provides the ability to dispense controlled sample sizes to the hybridization array for quantitative analysis while serving to increase the mass transfer rate and therefore reduce the overall analysis time. An automatic, electrokinetically based, single-nucleotide polymorphism (SNP) discrimination technique (that takes advantage of the combined effects of joule heating, applied potential field and the shear gradients within the double layer field on the thermodynamic stability of the target: probe complex) will also be described for the first time. The clinical utility of the technique will be demonstrated through the detection of genetic markers associated with spinal muscular atrophy, specifically the common C←T mutation in the SMN1 gene.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".