Selection and Identification of DNA Aptamers against Okadaic Acid for Biosensing Application
Bibliographic record
Abstract
This work describes the selection and identification of DNA aptamers that bind with high affinity and specificity to okadaic acid (OA), a lipophilic marine biotoxin that accumulates in shellfish. The aptamers selected using systematic evolution of ligands by exponential enrichment (SELEX) exhibited dissociation constants in the nanomolar range. The aptamer with the highest affinity was then used for the fabrication of a label-free electrochemical biosensor for okadaic acid detection. The aptamer was first immobilized on the gold electrode by a self-assembly approach through Au-S interaction. The binding of okadaic acid to the aptamer immobilized on the electrode surface induces an alteration of the aptamer conformation causing a significant decrease in the electron-transfer resistance monitored by electrochemical impedance spectroscopy. The aptasensor showed a linear range for the concentrations of OA between 100 pg/mL and 60 ng/mL with a detection limit of 70 pg/mL. The dissociation constant of okadaic acid with the aptamer immobilized on the electrode surface showed good agreement with that determined using fluorescence assay in solution. Moreover, the aptasensor did not show cross-reactivity toward toxins with structures similar to okadaic acid such as dinophysis toxin-1 and 2 (DTX-1, DTX-2). Further biosensing applications of the selected aptamers are expected to offer promising alternatives to the traditional analytical and immunological methods for OA detection.
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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 | 0.000 |
| Open science | 0.000 | 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".