Highly Sensitive RNA‐Cleaving DNAzyme Sensors from Surface‐to‐Surface Product Enrichment
Bibliographic record
Abstract
The engineering of easy-to-use biosensors with ultra-low detection sensitivity remains a major challenge. Herein, we report a simple approach for creating such sensors through the use of an RNA-cleaving DNAzyme (RcD) and a strategy designed to concentrate its cleavage product significantly. The assay uses micron-sized beads loaded with a target-responsive RcD and a paper strip containing a microzone covered with a DNA oligonucleotide capable of capturing the cleavage product of the RcD through Watson-Crick hybridization. Placing the beads and the paper strip in a target-containing test sample allows the bead-bound RcD molecules to undergo target-induced RNA cleavage, releasing a DNA fragment that is captured by the paper strip. This strategy, though simple, is very effective in achieving high levels of detection sensitivity, being able to enrich the concentration of the cleavage product by three orders of magnitude. It is also compatible with both fluorescence-based and colorimetric reporting mechanisms. This work provides a simple platform for developing ultrasensitive biosensors that take advantage of the widely available RcDs 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".