In Vitro Selection of a New Lanthanide-Dependent DNAzyme for Ratiometric Sensing Lanthanides
Bibliographic record
Abstract
Developing biosensors for lanthanides is an important but challenging analytical task. To address this problem, in vitro selection of RNA-cleaving DNAzymes was carried out using a library containing a region of 35 random nucleotides in the presence of Lu(3+), since Lu(3+) was reported to be the most efficient lanthanide for RNA cleavage. The resulting DNA sequences can be aligned to a single family with two conserved stretches of nucleotides. One of the representative DNAzymes (named Lu12) was further studied. Lu12 is more active with smaller lanthanides and has the lowest activity in the presence of the largest lanthanide (lutetium). Its cleavage rate is 0.12 min(-1) in the presence of 10 μM Nd(3+) at pH 6.0. This is a new DNAzyme, and a catalytic beacon sensor is designed by attaching a fluorophore/quencher pair, detecting Nd(3+) down to 0.4 nM (72 parts-per-trillion). This DNAzyme is highly selective for lanthanides as well, showing cleavage only with two nonlanthanide ions: Y(3+) and Pb(2+). We previously reported a DNAzyme named Ce13d, which has similar responses to all the trivalent lanthanides. Combining these two allows for a ratiometric assay that identifies a few large lanthanides.
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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 | 0.000 |
| 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".