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Record W2007451025 · doi:10.1021/ac202048y

DNA Adsorption to the Reservoir Walls Causing Irreproducibility in Studies of Protein–DNA Interactions by Methods of Kinetic Capillary Electrophoresis

2011· article· en· W2007451025 on OpenAlexafffund
Mirzo Kanoatov, Sergey N. Krylov

Bibliographic record

VenueAnalytical Chemistry · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCapillary electrophoresisAdsorptionDNACapillary actionKinetic energyElectrophoresisChromatographyThermodynamicsBiochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Methods of kinetic capillary electrophoresis (KCE) facilitate kinetic studies of protein-DNA interactions and highly efficient selection of DNA aptamers for protein targets. Here, we report a previously unnoticed source of error that affects the precision and accuracy of KCE-based measurements. The error manifests itself in cases that require the use of low concentrations of DNA. In such measurements, the reproducibility of the signal generated by the same fluorescently labeled DNA sample can have a relative standard deviation (RSD) as high as 40%. We have investigated the cause of the irreproducibility and found that it is attributed to DNA adsorption to the surface of the sample vials, in which protein-DNA mixtures are prepared prior to a KCE experiment. The use of commercially available "high DNA recovery" sample vials does not resolve the problem. We have found that the problem can be significantly alleviated by the passivation of the vial surface with blocking agents, such as masking DNA or bovine serum albumin (BSA). The described adsorption of DNA to the surface of sample vials may also be important in other procedures that deal with low DNA concentrations, such as aptamer selection and quantitative PCR.

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)0.001
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 score0.010
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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

Citations19
Published2011
Admission routes2
Has abstractyes

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