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Record W3206877406 · doi:10.1002/asia.202101091

Modulation of DNAzyme Activity via Butanol Dehydration

2021· article· en· W3206877406 on OpenAlexafffund
Yuqing Li, Mohamad Zandieh, Juewen Liu

Bibliographic record

VenueChemistry - An Asian Journal · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeChemistryButanolBiosensorMetal ions in aqueous solutionDNAn-ButanolBiomoleculeCombinatorial chemistryMetalNanotechnologyInorganic chemistryBiophysicsBiochemistryOrganic chemistryEthanolMaterials science

Abstract

fetched live from OpenAlex

Abstract Understanding the activity of biomolecules in cosolvent systems is important for catalysis, separation and developing biosensors. The majority of previously studied solvents are either phase separated with water or miscible with water. Butanol was recently used to extract water for the conjugation of DNA to gold nanoparticles. In this work, the effect of butanol on the activity of a few RNA‐cleaving DNAzymes was studied. A 130‐fold improvement in sensitivity for the Na + ‐specific EtNa DNAzyme was observed, and butanol also improved the activity of another Na + ‐specific DNAzyme, NaA43T by a few folds. However, when divalent metal ions were used for both EtNa and 17E DNAzymes, the activity was inhibited. A main driven force for enhanced DNAzyme activity is the concentration effect due to butanol dehydration. This study provides insights into the interplay between DNA, metal ions and organic solvents, and such an understanding might be useful for developing sensitive biosensors.

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)0.000
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 score0.035
Threshold uncertainty score0.479

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 head0.265
Teacher spread0.257 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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