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Inhibitory effect of silicon nanowires on the polymerase chain reaction

2012· article· en· W2140683412 on OpenAlexaff
Hongwei Wang, Lei Wang, Lin Yuan, Weikang Yang, John L. Brash, Hong Chen

Bibliographic record

VenueNanotechnology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNational Science Fund for Distinguished Young ScholarsPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsHorseradish peroxidaseDNAPolymerase chain reactionPolymeraseYield (engineering)Materials scienceMolecular biologyInhibitory postsynaptic potentialNanotechnologyBiochemistryEnzymeChemistryBiologyGene

Abstract

fetched live from OpenAlex

The effect of nanomaterials on biological reactions has received much attention. We report herein that silicon nanowires (SiNWs) inhibit the polymerase chain reaction (PCR). The inhibitory effect was found to be concentration-dependent, with a minimum inhibitory concentration of about 0.4 mg ml(-1). DNA polymerase, restriction endonucleases, lysozyme and horseradish peroxidase maintained their bioactivities after exposure to SiNWs. Also the interaction of SiNWs with primers and dNTP did not lead to decreased PCR yield. Compared to primers and dNTP, template DNA showed 4.7-10.5-fold greater adsorption on SiNWs. Template bound to SiNWs was ineffective in the PCR, whereas addition of free template to the PCR system increased the yield. The results of this work suggest that the inhibitory effect of SiNWs on the PCR was due to the selective adsorption of double-stranded DNA on SiNWs, thereby decreasing the availability of template for the reaction.

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 score0.022
Threshold uncertainty score0.358

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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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