Electrochemical and plasmonic detection methods yield comparable analytical performance for DNA hybridization
Bibliographic record
Abstract
DNA-based biosensors have been engineered for the measurement of different molecular targets. Depending on the means through which binding is transduced, this may introduce differences in analytical performance, especially when looking at the target's molecular weight and length. To address this question, we developed a combined approach of two commonly used transduction methods in DNA-based biosensors - electrochemistry and surface plasmon resonance - for concomitant surface interrogation. Specifically, we looked at the limits of detection and maximal responses of redox-reporter-modified DNA interfaces of increasing lengths binding to their complementary sequences. In doing so, along with comparable limits of detection, we observed that both methods produced similar sigmoidal target-responses that monotonically varied as a function of sequence length. We envision that our combined electrochemical-surface plasmon resonance (eSPR) approach showcases that SPR could be used as a first method to help engineer recognition elements before purchasing costly redox modifications, and in turn accelerate their translation into sensing platforms.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".