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Record W2131154423 · doi:10.1039/b605648m

DNA-duplexes containing abasic sites: correlation between thermostability and acoustic wave properties

2006· article· en· W2131154423 on OpenAlexafffund
Tibor Hianik, Xiaomeng Wang, S. Yu. Andreev, Nina G. Dolinnaya, T. S. Oretskaya, Michael Thompson

Bibliographic record

VenueThe Analyst · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAP siteThermostabilityDuplex (building)ChemistryDNANucleobaseBiotinylationNucleic acidDNA damageBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Aldehydic apurinic or apyrimidinic sites that lack a nucleobase moiety are one of the most common forms of toxic lesions in DNA. In the present study, a close structural analog of such a site, the 2-(hydroxymethyl) tetrahydrofuranyl residue, was synthesized in order to act as a model for damaged nucleic acid probes. Prepared oligodeoxyribonucleotides containing one, two or three abasic sites were hybridized to complementary sequences immobilized on a gold surface using the neutravidin-biotin interaction for study by thickness shear mode acoustic wave detector. Measurement of the complex electrical impedance of an AT-cut quartz device with immobilized biotinylated nucleotide allowed the detection of changes of series resonance frequency, Deltafs, and motional resistance, Rm, associated with duplex formation. The changes as detected by the acoustic wave method correlated well with the thermostability of DNA duplexes in solution. With respect to the latter, UV-monitored melting curves indicate that both the number of sites and their localization in the double-stranded structure influence the amount by which a 19 b.p. duplex is destabilized. The presence of 3 abasic sites completely destabilized the DNA duplex.

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 score0.053
Threshold uncertainty score0.329

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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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

Citations14
Published2006
Admission routes2
Has abstractyes

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