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Record W3008544219 · doi:10.1002/chem.202000245

Protein‐Mediated Suppression of Rolling Circle Amplification for Biosensing with an Aptamer‐Containing DNA Primer

2020· article· en· W3008544219 on OpenAlexafffund
Roger M. Bialy, M. Monsur Ali, Yingfu Li, John D. Brennan

Bibliographic record

VenueChemistry - A European Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Economic Development and Innovation
KeywordsRolling circle replicationAptamerPrimer (cosmetics)DNABiosensorFluorescenceTarget proteinBiologyMolecular biologySystematic evolution of ligands by exponential enrichmentBiochemistryChemistryDNA polymeraseRNAGene

Abstract

fetched live from OpenAlex

We report a method to detect proteins via suppression of rolling circle amplification (RCA) by using an appropriate aptamer as the linear primer (denoted as an aptaprimer) to initiate RCA. In the absence of a protein target, the aptaprimer is free to initiate RCA, which can produce long DNA products that are detected via binding of a fluorescent intercalating dye. Introduction of a target causes the primer region within the aptamer to become unavailable for binding to the circular template, inhibiting RCA. Using SYBR Gold or QuantiFluor dyes as fluorescent probes to bind to the RCA reaction product, it is possible to produce a generic protein-modulated RCA assay system that does not require fluorophore- or biotin-modified DNA species, substantially reducing complexity and cost of reagents. Based on this modulation of RCA, we demonstrate the ability to produce both solution and paper-based assays for rapid and quantitative detection of proteins including platelet derived growth factor and thrombin.

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 score0.016
Threshold uncertainty score0.576

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 head0.256
Teacher spread0.235 · 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

Citations36
Published2020
Admission routes2
Has abstractyes

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