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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".