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Record W2566017761 · doi:10.1002/cpnc.17

Rolling Circle Amplification with Chemically Modified Nucleoside Triphosphates

2016· article· en· W2566017761 on OpenAlexaff
Marcel Hollenstein, Masad J. Damha

Bibliographic record

VenueCurrent Protocols in Nucleic Acid Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of Bern
KeywordsRolling circle replicationTemplateCombinatorial chemistryPrimer extensionNucleaseNucleosideNucleic acidDNAPrimer (cosmetics)ChemistryLoop-mediated isothermal amplificationNucleotideContext (archaeology)NanotechnologyComputational biologyDNA replicationBiochemistryBiologyMaterials scienceBase sequenceOrganic chemistry

Abstract

fetched live from OpenAlex

Modified nucleoside triphosphates (dN*TPs) represent facile and versatile precursors for the introduction of chemical diversity into nucleic acids. While dN*TPs have been utilized in a plethora of practical applications, very little attention has been devoted to the assessment of their compatibility with isothermal amplification strategies. In this context, rolling circle amplification (RCA) is a wide-spread enzymatic replication method in which small single-stranded DNA (ssDNA) circles serve as templates in primer extension reactions yielding very long, ssDNA products. RCA is a pivotal tool for the generation of biosensor and diagnostic devices and is currently evaluated for its usefulness to create novel drug delivery systems. This unit describes the experimental procedures for the synthesis of modified RCA products using dN*TPs bearing chemical alterations at any possible location of the nucleosidic scaffold. Two ligation methods are presented for the generation of the DNA nanocircles that serve as templates for RCA, followed by a description of the RCA method itself and an assessment of the nuclease resistance of the ensuing products. © 2016 by John Wiley & Sons, Inc.

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 score0.037
Threshold uncertainty score0.808

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 head0.016
GPT teacher head0.296
Teacher spread0.280 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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