Aptamer-Based Electrochemical Biosensors for Marine Toxins
Bibliographic record
Abstract
Marine toxins are naturally occurring chemicals that can contaminate certain seafood. They are usually tasteless, odourless and have no toxic effect on shellfish. However, they exhibit toxicity towards marine mammals, birds, fish and humans. The ethical problems as well as the technical difficulties associated with the currently employed analysis methods for marine toxins are encouraging the research for suitable alternatives to be applied in a regulatory monitoring regime. Here, we report electrochemical biosensing platforms for okadiac acid (OA) as well as brevetoxin-2 (BTX-2) detection using aptamers as specific receptors. Using in vitro selection, high affinity DNA aptamers to OA and BTX-2 were successfully selected for the first time from a large pool of random sequences. The binding of the toxins to aptamer pools/clones was monitored using fluorescence and electrochemical impedance spectroscopy (EIS). Most of the selected aptamers exhibited high binding affinity to their target toxins with a dissociation constants in the nanomolar range. The effects of the incubation time, pH and metal ions concentrations on the aptamers-toxins binding were studied. The highest affinity aptamers were then used to construct label-free impedimetric biosensors for both OA and BTX-2 detection showing good sensitivity. A high degree of cross reactivity of the selected BTX aptamers to the two similar congeners, BTX-2 and BTX-3 was observed, whereas no cross reactivity to other marine toxins was obtained. Moreover, the aptasensors were applied for the detection of OA and BTX-2 in spiked shellfish extracts showing very good recovery percentages. We believe that the continual emergence of novel, high-affinity aptamers will open the way to a variety of biosensing architectures, particularly for small-molecule toxin detection in complex samples.
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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) | 0.001 | 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".