Development of an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) aptasensor for direct detection of DNA hybridization
Bibliographic record
Abstract
Rapid detection of disease biomarkers at the patient point-of-care is essential to timely and effective treatment. The research described herein focuses on the development of an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) DNA aptasensor capable of direct detection of tuberculosis (TB) DNA. Specifically, a plausible DNA biomarker present in TB patient urine was chosen as the model target for detection. Cost-effective screen printed electrodes (SPEs) modified with silver nanoparticles (AgNP) were used as the aptasensor platform, onto which the aptamer specific for the target DNA was immobilized. Direct detection of the target DNA was demonstrated through the appearance of SERS peaks characteristic for adenine, present only in the target strand. Modulation of the applied potential allowed for a sizeable increase in the observed SERS response and the use of thiol back-filling prevented non-specific adsorption of non-target DNA. To our knowledge, this work represents the first EC-SERS study of an aptasensor for the direct, label-free detection of DNA hybridization. Such a technology paves the way for rapid detection of disease biomarkers at the patient point-of-care.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".