Evidence map›Paper›PMID 23519610›Full record

ArticleNucleic acids research2013

The effect of hybridization-induced secondary structure alterations on RNA detection using backscattering interferometry.

Nicholas M Adams, Ian R Olmsted, Frederick R Haselton, Darryl J Bornhop, David W Wright

Abstract read
In one paragraph

Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Origin and prediction of free-solution interaction studies performed label-free.Proceedings of the National Academy of Sciences of the United States of America · 2016
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nicholas M AdamsDepartment of Chemistry, Vanderbilt University, Nashville, TN 37235, USA.
Ian R Olmsted
Frederick R Haselton
Darryl J Bornhop
David W Wright

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Backscattering interferometry (BSI) has been used to successfully monitor molecular interactions without labeling and with high sensitivity. These properties suggest that this approach might be useful for detecting biomarkers of infection. In this report, we identify interactions and characteristics of nucleic acid probes that maximize BSI signal upon binding the respiratory syncytial virus nucleocapsid gene RNA biomarker. The number of base pairs formed upon the addition of oligonucleotide probes to a solution containing the viral RNA target correlated with the BSI signal magnitude. Using RNA folding software mfold, we found that the predicted number of unpaired nucleotides in the targeted regions of the RNA sequence generally correlated with BSI sensitivity. We also demonstrated that locked nucleic acid (LNA) probes improved sensitivity approximately 4-fold compared to DNA probes of the same sequence. We attribute this enhancement in BSI performance to the increased A-form character of the LNA:RNA hybrid. A limit of detection of 624 pM, corresponding to ∼10(5) target molecules, was achieved using nine distinct ∼23-mer DNA probes complementary to regions distributed along the RNA target. Our results indicate that BSI has promise as an effective tool for sensitive RNA detection and provides a road map for further improving detection limits.

Indexed as

Nucleic Acid HybridizationInterferometryNucleic Acid ConformationNucleocapsid ProteinsOligonucleotide ProbesOligonucleotidesRNARNA FoldingRNA, Virallocked nucleic acidNucleocapsid ProteinsOligonucleotide ProbesOligonucleotidesRNARNA, Viral

Identifiers

PMID23519610
PMCPMC3643578

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.