Aptamer-based biosensing platforms for saxitoxin and tetrodotoxin: Advances, challenges, and future perspectives in food safety and environmental monitoring
Bibliographic record
Abstract
Saxitoxin (STX) and tetrodotoxin (TTX) are among the most potent marine neurotoxins, posing severe risks to public health and the seafood industry. Their high toxicity, structural diversity, and occurrence in complex aquatic and food matrices pose significant challenges for reliable detection and quantification. Conventional instrumental analysis methods offer high sensitivity and specificity but require costly instrumentation, skilled personnel, and time-consuming sample preparation. Immunoassays, while faster, may suffer from limited recognition of toxin congeners. In recent years, aptamers, synthetic single-stranded DNA or RNA sequences have emerged as promising alternatives to antibodies for toxin recognition. Aptamer-based biosensing platforms offer advantages in terms of stability, reproducibility, ease of modification, and scalability. A broad range of detection techniques has been developed, including optical, electrochemical and hybrid systems, often incorporating nanomaterials and signal amplification strategies to achieve ultralow detection limits in food and environmental samples. Yet, the path from promising laboratory prototypes to reliable field tools remains challenging, particularly when matrix effects compromise sensor robustness. This review provides a comprehensive overview of aptamer selection strategies for STX and TTX, recent advances in biosensing technologies, and the performance of various platforms in different matrices. Key challenges, including matrix effects, technological feasibility, and the need for compliance with official regulations, are discussed. Finally, perspectives for developing robust, field-deployable aptasensors are outlined, emphasizing their potential to enable rapid, sensitive, and cost-effective toxin monitoring for food safety and environmental protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".