Impedance Sensing of DNA Binding Drugs Using Gold Substrates Modified with Gold Nanoparticles
Bibliographic record
Abstract
Interfacial interactions between immobilized DNA probes and DNA-specific sequence binding drugs were investigated using impedance spectroscopy toward the development of a novel biosensing scheme. The impedance measurements are based on the charge-transfer kinetics of the [Fe(CN)6]3-/4- redox couple. Compared to bare gold surfaces, the immobilization of DNA and then the DNA-drug interaction on electrode surfaces altered the capacitance and the interfacial electron resistance and thus diminished the charge-transfer kinetics by reducing the active area of the electrode or by preventing the redox species from approaching the electrode. Electrochemical deposition of gold nanoparticles on a gold electrode surface showed significant improvement in sensitivity. DNA-capped gold nanoparticles on electrodes act as selective sensing interfaces with tunable sensitivity due to higher amounts of DNA probes and the concentric orientation of the DNA self-assembled monolayer. The specificity of the interactions of two classical minor groove binders, mythramycin, a G-C specific-DNA binding anticancer drug, netropsin, an A-T specific-DNA binding drug and an intercalator, nogalamycin on AT-rich DNA-modified substrate and GC-rich DNA-modified substrate are compared. Using gold nanoparticle-deposited substrates, impedance spectroscopy resulted in a 20-40-fold increase in the detection limit. Arrays of deposited gold nanoparticles on gold electrodes offered a convenient tool to subtly control probe immobilization to ensure suitably adsorbed DNA orientation and accessibility of other binding molecules.
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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) | 0.001 | 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 | 0.001 |
| 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".