Femtomolar and selective dopamine detection by a gold nanoparticle enhanced surface plasmon resonance aptasensor
Bibliographic record
Abstract
ABSTRACT Ultrasensitive and selective detection and quantification of dopamine (DA) plays a key role in monitoring neurodegenerative diseases. However, the detection limit reported for DA detection is typically in the lower nM range. Pushing the detection limit to pM or lower for this particular target to cover the physiological levels (< 130 pM) is significant. Herein, DA DNA aptamer (DAAPT) gold nanoparticle (AuNP) conjugate is utilized to enhance the surface plasmon resonance (SPR) signal, which enables to detect and quantify DA in the femtomolar (200 fM) to picomolar range. To the best of our knowledge, this is the lowest detection limit achieved for SPR sensing of dopamine. The as-prepared 10 nm DAAPT-AuNP conjugate demonstrates strong binding affinity (K d = 3.1 ± 1.4 nM) to the complementary DNA (cDNA) probe on gold chip. The cDNA probe is immobilized to the chip via polydopamine surface chemistry, which allows the Michael addition of any primary amine-terminated biomolecules. By adjusting the concentration of the DAAPT-AuNP conjugate, two calibration curves are generated with dynamic ranges from 100 µM to 2 mM, and from 200 fM to 20 nM, respectively. Both calibration curves have negative slopes, showing good agreement to a dose-response curve in an enzyme inhibition assay. In addition, the sensing strategy is evaluated to be specific for DA detection using a series of DA analogs and other metabolites as potential interferences.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".