A single DNA aptamer functions as a biosensor for ricin
Why this work is in the frame
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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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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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 it