Efficient signaling platforms built from a small catalytic DNA and doubly labeled fluorogenic substrates
Bibliographic record
Abstract
RNA-cleaving deoxyribozyme 8-17 has been increasingly used in nanotechnology and biosensing applications. Conventional methods to equip 8-17 with fluorescent signaling property usually involve covalent attachment of two dyes at nucleotide positions that are far away from the catalytic core, such that the bulky dye structures would not affect the deoxyribozyme activity. However, the maximum fluorescent enhancement associated with these 8-17 constructs is typically < or =10-fold, due to a high fluorescent background. To find an optimal balance between signal enhancement and signaling speed, we have conducted a comprehensive study on the effects of the nature of dyes (Alexa Fluor 488, 546 and 647; QSY 9 and 21) as well as their attaching positions along the substrate strand on the catalytic and signaling performance of 8-17. Our results have indicated that 8-17 is able to cleave almost every modified substrate, including those that have chromophores only 1 nt away from the cleavage site. Most importantly, almost all of these substrates are able to generate 15- to 85-fold signal enhancement within 10 min. We have also provided guidelines for selecting substrates that could offer the best signal enhancement, the fastest signaling speed, or the best balance between signal enhancement and signaling speed.
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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".