An Ultrasensitive Light-up Cu<sup>2+</sup> Biosensor Using a New DNAzyme Cleaving a Phosphorothioate-Modified Substrate
Bibliographic record
Abstract
Cu(2+) is a very important metal ion in biology, environmental science, and industry. Developing biosensors for Cu(2+) is a key topic in analytical chemistry. DNAzyme-based sensors are highly attractive for their excellent sensitivity, stability, and programmability. In the past decade, a few Cu(2+) biosensors were reported using DNAzymes with DNA cleavage or DNA ligation activity. However, they require unstable ascorbate or imidazole activation. So far, no RNA-cleaving DNAzymes specific for Cu(2+) are known. In this work, a phosphorothioate (PS) RNA-containing library was used for in vitro selection, and a few new Cu(2+)-specific RNA-cleaving DNAzymes were isolated. Among them, a DNAzyme named PSCu10 was studied further. It has only eight nucleotides in the enzyme loop with a cleavage rate of 0.1 min(-1) in the presence of 1 μM Cu(2+) at pH 6.0 (its optimal pH). Between the two diastereomers of the PS RNA chiral center, the R(p) isomer is 37 times more active than the S(p) one. Among the other divalent metal ions, only Hg(2+) can cleave the substrate due to its extremely high thiophilicity. A catalytic beacon sensor was designed with a detection limit of 1.6 nM Cu(2+) and extremely high selectivity. PSCu10 is specific for Cu(2+), and it has no cleavage in the presence of ascorbate, which reduces Cu(2+) to Cu(+).
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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) | 0.001 | 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 | 0.001 |
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".