Paralytic shellfish poisoning (PSP) toxin binders for optical biosensor technology: problems and possibilities for the future: a review
Bibliographic record
Abstract
This review examines the developments in optical biosensor technology, which uses the phenomenon of surface plasmon resonance, for the detection of paralytic shellfish poisoning (PSP) toxins. Optical biosensor technology measures the competitive biomolecular interaction of a specific biological recognition element or binder with a target toxin immobilised onto a sensor chip surface against toxin in a sample. Different binders such as receptors and antibodies previously employed in functional and immunological assays have been assessed. Highlighted are the difficulties in detecting this range of low molecular weight toxins, with analogues differing at four chemical substitution sites, using a single binder. The complications that arise with the toxicity factors of each toxin relative to the parent compound, saxitoxin, for the measurement of total toxicity relative to the mouse bioassay are also considered. For antibodies, the cross-reactivity profile does not always correlate to toxic potency, but rather to the toxin structure to which it was produced. Restrictions and availability of the toxins makes alternative chemical strategies for the synthesis of protein conjugate derivatives for antibody production a difficult task. However, when two antibodies with different cross-reactivity profiles are employed, with a toxin chip surface generic to both antibodies, it was demonstrated that the cross-reactivity profile of each could be combined into a single-assay format. Difficulties with receptors for optical biosensor analysis of low molecular weight compounds are discussed, as are the potential of alternative non-antibody-based binders for future assay development in this area.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".