Scanning positional variations in single-nucleotide polymorphism of DNA: an electrochemical study
Bibliographic record
Abstract
While there are a number of electrochemical methods reported that enable the detection of single nucleotide mismatches, the determination of mismatch position in a double stranded DNA remains an unsolved challenge. Using a model system, we systematically explored the electrochemical response of all possible positions of single nucleotide mismatches in a set of 25-mer DNA films. These ds-DNA sequences each with a single mismatch at one of the twenty-five positions were bound to gold surfaces through a Au-S linkage and analyzed by electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM) in the absence and presence of Zn(2+). We expected a unique response from each mismatched sequence in order to discriminate the mismatch positions. A pattern emerges between the electrochemical signals and mismatch positions. The positions can be grouped broadly into positions that exhibit large differences between matched and mismatched DNA (around positions 5 and 9) and those that exhibit smaller differences (around positions 1, 13 and 23) in the charge transfer resistance ΔR(ct), evaluated by EIS, and the apparent rate constant k(0), evaluated by SECM. To the best of our knowledge, this is the first study evaluating the electrochemical response of a single nucleotide mismatch as a function of mismatch positions along an oligonucleotide sequence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".