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Record W2413713901 · doi:10.1002/chem.201601426

In Vitro Selection of Chromium‐Dependent DNAzymes for Sensing Chromium(III) and Chromium(VI)

2016· article· en· W2413713901 on OpenAlexafffund
Wenhu Zhou, Mahsa Vazin, Tianmeng Yu, Jinsong Ding, Juewen Liu

Bibliographic record

VenueChemistry - A European Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsChromiumDeoxyribozymeSelection (genetic algorithm)In vitroChemistryEnvironmental chemistryMaterials scienceMetallurgyComputer scienceDNABiochemistryArtificial intelligence

Abstract

fetched live from OpenAlex

Chromium is a very important analyte for environmental monitoring, and developing biosensors for chromium is a long-standing analytical challenge. In this work, in vitro selection of RNA-cleaving DNAzymes was carried out in the presence of Cr(3+) . The most active DNAzyme turned out to be the previously reported lanthanide-dependent Ce13d DNAzyme. Although the Ce13d activity was about 150-fold lower with Cr(3+) than that with lanthanides, the activity of lanthanides and other competing metals was masked by using a phosphate buffer; this left Cr(3+) as the only metal that could activate Ce13d. With 100 μm Cr(3+) , the cleavage rate is 1.6 h(-1) at pH 6. By using a molecular beacon design, Cr(3+) was measured with a detection limit of 70 nm, which was significantly lower than the United States Environmental Protection Agency (EPA) limit (11 μm). Cr(4+) was measured after reduction by NaBH4 to Cr(3+) , and it could be sensed with a similar detection limit of 140 nm Cr(4+) ; this value was lower than the EPA limit of 300 nm. This sensor was tested for chromium speciation analysis in a real sample, and the results supported its application for environmental monitoring. At the same time, it has enhanced our understanding of the interactions between chromium and DNA.

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.

How this classification was reachedexpand

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)0.001
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 score0.011
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.244
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2016
Admission routes2
Has abstractyes

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