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Record W2153398083 · doi:10.1039/c0an00184h

Probing nucleobase mismatch variations by electrochemical techniques: exploring the effects of position and nature of the single-nucleotide mismatch

2010· article· en· W2153398083 on OpenAlexafffund
Mohtashim H. Shamsi, Heinz‐Bernhard Kraatz

Bibliographic record

VenueThe Analyst · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNucleobaseNucleotidePosition (finance)ChemistryElectrochemistryDNABiochemistryPhysical chemistryElectrodeGene

Abstract

fetched live from OpenAlex

Electrochemical impedance spectroscopy (EIS) has been used as an ultrasensitive tool for label-free detection of single-nucleotide mismatches in double-stranded DNA (ds-DNA) films. In this study, we have explored the effects of the position and of the type of single-nucleotide mismatch in ds-DNA on gold surfaces and were able to distinguish mismatch positions and mismatch pairs. The single-nucleotide mismatches A-C, A-A and A-G were introduced at three positions within the sequence in bottom, middle and top positions of ds-DNA, the films were studied by EIS, and the impedance results were interpreted with the help of equivalent circuits. The DeltaR(ct), the difference in charge transfer resistance before and after the addition of Zn(2+), was used to distinguish single-nucleotide mismatch within the DNA sequences. Importantly, the mismatch pair is easily distinguishable at the middle position. A purine-pyrimidine mismatch can be distinguished from purine-purine mismatch by its lower DeltaR(ct) value. In addition, all ds-DNA films were studied by scanning electrochemical microscopy in the absence and presence of Zn(2+), allowing us to distinguish a range of mismatched films from matched ds-DNA film.

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 score0.001
Threshold uncertainty score0.267

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 head0.004
GPT teacher head0.221
Teacher spread0.217 · 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

Citations28
Published2010
Admission routes2
Has abstractyes

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