A single DNA aptamer functions as a biosensor for ricin
Bibliographic record
Abstract
The use of microorganisms or toxins as weapons of death and fear is not a novel concept; however, the modes by which these agents of bioterrorism are deployed are increasingly clever and insidious. One mechanism by which biothreats are readily disseminated is through a nation's food supply. Ricin, a toxin derived from the castor bean plant, displays a strong thermostability and remains active at acidic and alkaline pHs. Therefore, the CDC has assigned ricin as a category B reagent since it may be easily amendable as a deliberate food biocontaminate. Current tools for ricin detection utilize enzymatic activity, immunointeractions and presence of castor bean DNA. Many of these tools are confounded by complex food matrices, display a limited dynamic range of detection and/or lack specificity. Aptamers, short RNA and single stranded DNA sequences, have increased affinity to their selected receptors, experience little cross-reactivity to other homologous compounds and are currently being sought after as biosensors for bacterial contaminants in food. This paper describes the selection and characterization of a single, dominant aptamer, designated as SSRA1, against the B-chain of ricin. SSRA1 displays one folding conformation that is stable across 4-63 °C (ΔG = -5.05). SSRA1 is able to concentrate at least 30 ng mL(-1) of ricin B chain from several liquid food matrices and outcompetes a currently available ELISA kit and ricin aptamer. Furthermore, we show detection of 25 ng mL(-1) of intact ricin A-B complex using SSRA1 combined with surface enhanced Raman scattering technique. Thus, SSRA1 would serve well as pre-analytical tool for processing of ricin from liquid foods to aid current diagnostics as well as a sensor for direct ricin 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) | 0.000 | 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 | 0.001 |
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".