Ultrasensitive DNA detection based on target-triggered rolling circle amplification and fluorescent poly(thymine)-templated copper nanoparticles
Bibliographic record
Abstract
We describe a novel strategy for the ultrasensitive detection of target DNA based on rolling circle amplification (RCA) coupled with fluorescent poly(thymine)-templated copper nanoparticles (poly T-CuNPs). In the presence of target DNA, a padlock DNA probe that consists of two regions: a target DNA-specific region and a poly(adenine) region, is circularized by the ligation reaction, and the subsequent RCA reaction is promoted to generate long, concatemeric, single-stranded DNA (ssDNA) with a lot of repetitive poly T sequences. As a result, a large number of poly T-CuNPs are formed, exhibiting a highly fluorescent signal. However, in the absence of target DNA or in the presence of non-specific target DNA, the padlock DNA probe is not circularized and the subsequent RCA is not executed, leading to no production of fluorescent poly T-CuNPs. With this simple strategy, we successfully analyzed the target DNA with the ultralow detection limit of 7.79 aM, a value that is 3 or 7 orders of magnitude lower than those of previous RCA-based fluorescent DNA detection strategies. In addition, the developed system was demonstrated to selectively discriminate non-specific target DNAs with one-base mismatch, suggesting potential application in the accurate diagnosis of single nucleotide polymorphisms or mutations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".