Quality Control Certification of RNA Aptamer‐Based Detection
Bibliographic record
Abstract
Aptamers are single-stranded DNA or RNA molecules with a defined tertiary structure for molecular recognition. Numerous RNA aptamers with excellent binding affinity and specificity have been reported; they constitute an attractive reservoir of molecular recognition elements for biosensor development. However, RNA is relatively unstable owing to spontaneous hydrolysis and nuclease degradation. Thus, RNA aptamer-based biosensors are prone to producing false-positive signals. Here, we present an RNA aptamer biosensor design strategy that utilises an internal control to distinguish target binding from false-positive signals. The sequence of a chosen RNA aptamer is expanded so that it can form three consecutive short RNA-DNA duplexes with 1) a quencher-labelled DNA strand (Q(1)DNA), 2) a dual-fluorophore-labelled DNA strand (F(1)DNAF(2)) and 3) another quencher-labelled DNA strand (Q(2)DNA). The addition of a target releases Q(2)DNA from the duplex assembly, and produces the expected positive signal from F(2). However, the authenticity of target recognition is validated only if no signal is generated from F(1). We have successfully engineered two fluorescent reporters by using an RNA aptamer that binds thrombin and one that binds theophylline. Both reporters show the expected binding affinity and specificity, and are capable of reporting system malfunction when treated with nucleases and chemical denaturants. This strategy provides a simple and reliable way to ensure high-quality detection when RNA aptamers are employed as molecular-recognition elements.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".