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Record W2254080975 · doi:10.14447/jnmes.v15i1.82

A New Electrochemical Biosensor of Host-guest Recognition Mode for DNA Hybridization Detection

2011· article· en· W2254080975 on OpenAlexvenueno aff
Hui Zhao, Hao Fan, JinXiang Ceng, Qianggen Li, Yan Lin, Xiaoyun Wang

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDNABiosensorChemistryElectrodeElectrochemistryDNA–DNA hybridizationHybridization probeMoleculeMolecular recognitionCombinatorial chemistryMolecular biologyNanotechnologyBiophysicsBiochemistryMaterials scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

This paper reports on an electrochemical biosensor to detect the hybridization specificity by using host-guest recognition technique. A hairpin DNA with a dabcyl molecule which is typical guest molecule to b-Cyclodextrins (b-CD) at the 3’-terminus and a NH2 linked at the 5’-terminus as the probe DNA. The probe DNA was immobilized on the PdS nanoparticle to construct a double-labeled probe (DLP) and could selectively hybridize with its target DNA. A b-CD modified Poly(N-acetylaniline) glassy carbon electrode was used for capturing dabcyl in DLP. Without binding with target DNA, the DLP keep stem-loop structure and block dabcyl enter into the cavity of b-CD on electrode. However, a target-binding DLP is incorporated into double stranded DNA, causing loop-stem structure opened and dabcy l could be easy captured by b-CD which brought DLP on electrode surface. With electrochemical measurement, the signal come from Pd2+ be used for target DNA quantitative analysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.267
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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