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Record W2790989652 · doi:10.1139/cjc-2017-0544

A colorimetric method for the sequence-specific recognition of double-stranded DNA on the surface of a silver-coated glass slide

2018· article· en· W2790989652 on OpenAlexvenueno aff
Xiaoting Guo, Jing Wang, Zhifang Zhu, Manjun Zhang, Haigang Li, Jianmin Liu, Liansheng Ling

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryAbsorbanceHorseradish peroxidaseDNAStreptavidinDetection limitSequence (biology)SelectivityChromatographyPeroxidaseEnzymeBiochemistryBiotinCatalysis

Abstract

fetched live from OpenAlex

In this study, a colorimetric method for sequence-specific recognition of double-stranded DNA (dsDNA) was established on the surface of a silver-coated glass slide. Oligo-1 was assembled on the surface of a silver-coated glass slide through an Ag–S bond, and Oligo-2 as reporter was used to bind with streptavidin-horseradish peroxidase (SA–HRP). They could bind with target dsDNA that was composed of Oligo-3 and Oligo-4 on the surface of a silver-coated glass slide through triplex formation. The bound HRP could be moved into the solution by DNase I and catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB). Therefore, the concentration of target dsDNA could be determined with the colour change of TMB. Under the optimum conditions, the absorbance was proportional to the concentration of target dsDNA over the range of 100 pmol/L to 2.0 nmol/L, with a detection limit of 13 pmol/L. In addition, this method showed good sequence selectivity, enabling it to be further developed for the detection of other polymerase chain reaction (PCR) products.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.045
GPT teacher head0.297
Teacher spread0.252 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

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