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Record W2802718297 · doi:10.1039/c8an00232k

Hg(<scp>ii</scp>) interactions with T-rich regions in oligonucleotides: effects of positional variations on the electrochemical properties

2018· article· en· W2802718297 on OpenAlexafffund
Ajar Kamal, Renu Sharma, Zhe She, Heinz‐Bernhard Kraatz

Bibliographic record

VenueThe Analyst · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto ScarboroughNatural Sciences and Engineering Research Council of Canada
KeywordsElectrochemistryOligonucleotideChemistryCombinatorial chemistryDNAPhysical chemistryBiochemistryElectrode

Abstract

fetched live from OpenAlex

Hg(ii) binding to thymine-rich oligonucleotides (ODNs) is investigated electrochemically. The focus of this study is to probe the effects of position on the electrochemical response. For this purpose, three oligonucleotides were investigated in which the position of a hexa-thymine repeat is varied within a surface-supported oligonucleotide. The hexa repeats were placed in the top, middle, and bottom positions within the strand with respect to the gold surface. The effects were monitored by electrochemical impedance spectroscopy and scanning electrochemical microscopy. Using charge transfer resistance (RCT) and tip current (I) as a measure, it was possible to monitor the effects of Hg(ii) binding to the ds-oligonucleotide. The extent of film resistance reduces as the T-rich region moves from the bottom to top position within the film. The T-rich region closer to the gold surface probably builds less flexible and more rigid T-Hg(ii)-T basepairs compared to the other two positions and is expected to stay in the upright orientation on the surface. This in turn results in significant differences in the electrochemical readout, demonstrating that the position of T-rich sequences within an oligonucleotide strand matters.

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 score0.005
Threshold uncertainty score0.234

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 head0.240
Teacher spread0.232 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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