A General Strategy to Create RNA Aptamer Sensors Using “Regulated” Graphene Oxide Adsorption
Bibliographic record
Abstract
Aptamers have been used as molecular recognition elements for sensor development in combination with graphene oxide (GO), a nanomaterial with properties including fluorescence quenching, and selective adsorption of single-stranded nucleic acids. However, previous sensor designs based on aptamer-GO adsorption have not demonstrated wide applicability, and few studies have explored the potential of RNA aptamers. Herein, we present a new sensing strategy based on "regulated" GO adsorption that can accommodate various RNA aptamers. First, adsorption of a fluorophore-labeled RNA aptamer to GO results in fluorescence quenching due to close proximity of the fluorophore to GO. The addition of a complementary, "blocking" DNA strand (BDNA) that hybridizes to the 3'-end of the aptamer, weakens aptamer-GO interaction, and enables increased fluorescent signal generation upon the addition of target, as the sensing system becomes completely separated from GO. Our findings can be applied toward different aptamers, and adapted to enhance generality of existing sensing 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) | 0.001 | 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 | 0.001 | 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".