Mechanistic insights into antibody recognition of tetrodotoxin analogues: Implications for neurotoxicological assessment
Bibliographic record
Abstract
Tetrodotoxins (TTXs) pose significant food safety risks due to their potent neurotoxicity. Growing concerns about the impact of these toxins on public health have driven the development of new detection methods, with immunoassays showing strong potential. However, limited knowledge of the cross-reactivity of anti-TTX antibodies with analogues may compromise the reliability of these assays in food safety applications. To address this, cross-reactivity factors (CRFs) for five TTX analogues (i.e., 11-norTTX-6(S)-ol, 11-deoxyTTX, 6,11-dideoxyTTX, 5,11-dideoxyTTX, and 5,6,11-trideoxyTTX) were assessed using a magnetic bead-based immunoassay. In parallel, the antibody's ability to neutralise the toxicity of TTX analogues was evaluated in Neuro-2a cells using automated patch clamp, a single-cell biosensing platform specifically designed for in vitro toxicity assessment and characterisation. Antibody cross-reactivity towards the tested analogues correlated with their relative toxicity, enabling a selective detection of the most hazardous compounds. These findings highlight the dual role of molecular structure in dictating both toxicological potency and immunological recognition, and support the use of immunoassays as effective tools for TTX monitoring in food safety applications.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".