MétaCan
Menu
Back to cohort
Record W2523646342 · doi:10.1093/nar/gkw845

A highly specific sodium aptamer probed by 2-aminopurine for robust Na<sup>+</sup>sensing

2016· article· en· W2523646342 on OpenAlexaff
Wenhu Zhou, Jinsong Ding, Juewen Liu

Bibliographic record

VenueNucleic Acids Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAptamerBiophysicsBiologyFluorescenceDeoxyribozymeDNAStackingNucleic acidIonic strengthQuenching (fluorescence)Cleavage (geology)G-quadruplexFörster resonance energy transferBiochemistryMolecular biologyChemistry

Abstract

fetched live from OpenAlex

Sodium is one of the most abundant metals in the environment and in biology, playing critical ecological and physiological roles. Na+ is also the most common buffer salt for nucleic acids research, while its specific interaction with DNA has yet to be fully studied. Herein, we probe a highly selective and robust Na+ aptamer using 2-aminopurine (2AP), a fluorescent adenine analog. This aptamer has two DNA strands derived from the Ce13d DNAzyme. By introducing a 2AP at the cleavage site of the substrate strand, Na+ induces ∼40% fluorescence increase. The signaling is improved by a series of rational mutations, reaching >600% with the C10A20 double mutant. This fluorescence enhancement suggests relaxed base stacking near the 2AP label upon Na+ binding. By replacing a non-conserved adenine in the enzyme strand by 2AP, Na+-dependent fluorescence quenching is observed, suggesting that the enzyme loop folds into a more compact structure upon Na+ binding. The fluorescence changes allow for Na+ detection. With an optimized sequence, a detection limit of 0.4 mM Na+ is achieved, reaching saturated signal in less than 10 s. The sensor response is insensitive to ionic strength, which is critical for Na+ detection.

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 score0.201
Threshold uncertainty score0.794

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 studies0.0000.001
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 head0.037
GPT teacher head0.318
Teacher spread0.281 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueNucleic Acids ResearchSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207