Label-Free Electrochemical Aptasensor for the Sensitive Detection of Cyanotoxin Anatoxin-a
Bibliographic record
Abstract
The sensitive detection of neurotoxin anatoxin-a (ATX) is a necessary to effectively manage and control the treatment of water resources. Here we report the selection of high affinity DNA aptamers targeting ATX for developing an impedimetic aptasensor for ATX for which there are no reported biosensors so far. The dissociation constants (Kd) of the aptamers are in the nanomolar range. The sensor was designed by self assembling of disulfide modified aptamer on a gold electrode. The aptasensor fabrication process was characterized using cyclic voltammetry and electrochemical impedance spectroscopy. Upon ATX recognition to the immobilized aptamer, a marked decrease in the electron-transfer resistance was recorded. This is as a result of the aptamer conformation change which is used as sensor signal. The aptasensor showed a limit of detection of 0.5nM and a wide linear range for ATX concentrations between 1nM and 100nM. The Kd that was calculated from the aptasensor signal showed a lower value implying that the anchoring of the aptamer on the Au surface enhanced its affinity to ATX. The ATX aptasensor showed high stability as well as high specificity against common cynaobacterial toxins. Further biosensor designs will be expected using those aptamers for simple ATX 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 | 0.000 |
| 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 | 0.001 |
| 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".