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Record W2299532471 · doi:10.1021/acs.analchem.6b00327

A Silver DNAzyme

2016· article· en· W2299532471 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeChemistryFluorophoreCatalysisBiosensorFluorescenceMetal ions in aqueous solutionDivalentMetalDetection limitRNANanotechnologyInorganic chemistryCombinatorial chemistryChromatographyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Silver is a very common heavy metal, and its detection is of significant analytical importance. DNAzymes are DNA-based catalysts; they typically recruit divalent and trivalent metal ions for catalysis. Herein, we report a silver-specific RNA-cleaving DNAzyme named Ag10c obtained after six rounds of in vitro selection. Ag10c displays a catalytic rate of 0.41 min(-1) with 10 μM Ag(+) at pH 7.5 with 200 mM NaNO3, while its activity is completely inhibited with the same concentration of NaCl. Ag10c is highly specific for Ag(+) among all the tested metals. A catalytic beacon biosensor is designed by labeling a fluorophore and a quencher on the DNAzyme. Fluorescence enhancement is observed in the presence of Ag(+) with a detection limit of 24.9 nM Ag(+). The sensor shows a similar analytical performance in Lake Huron water. This is the first monovalent transition metal dependent RNA-cleaving DNAzyme. Apart from its biosensor application, this study strengthens the idea of exploring beyond the traditional understanding of multivalent ion dependent DNAzyme catalysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.264
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it