Evidence map›Paper›PMID 26551436›Full record

ArticleAnalytical chemistry2015

Enhancing Protein Capture Using a Combination of Nanoyeast Single-Chain Fragment Affinity Reagents and Alternating Current Electrohydrodynamic Forces.

Ramanathan Vaidyanathan, Sakandar Rauf, Yadveer S Grewal, Lauren J Spadafora, Muhammad J A Shiddiky, Gerard A Cangelosi, Matt Trau

Open access · greenAbstract read
In one paragraph

Article in Analytical chemistry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
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

3 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

7 authors at 2 institutions in 2 countries.

Ramanathan VaidyanathanCentre for Personalized NanoMedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), Corner College and Cooper Roads (Bldg 75), The University of Queensland , Brisbane, Queensland 4072, Australia.
Sakandar RaufCentre for Personalized NanoMedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), Corner College and Cooper Roads (Bldg 75), The University of Queensland , Brisbane, Queensland 4072, Australia.
Yadveer S GrewalCentre for Personalized NanoMedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), Corner College and Cooper Roads (Bldg 75), The University of Queensland , Brisbane, Queensland 4072, Australia.
Lauren J SpadaforaSchool of Public Health, University of Washington , Seattle, Washington 98195, United States.
Muhammad J A ShiddikyCentre for Personalized NanoMedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), Corner College and Cooper Roads (Bldg 75), The University of Queensland , Brisbane, Queensland 4072, Australia.
Gerard A CangelosiSchool of Public Health, University of Washington , Seattle, Washington 98195, United States.
Matt TrauCentre for Personalized NanoMedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), Corner College and Cooper Roads (Bldg 75), The University of Queensland , Brisbane, Queensland 4072, Australia.
University of Queensland · AUUniversity of Washington · US

Funding

Diseases of Public Health Importance Training GrantT32AI007509 · NIAID · UNIVERSITY OF WASHINGTON · PI LUND, JENNIFER M · 1997 to 2024
$6.3M
Accelerated molecular probe pipelineU01AI082186 · NIAID · UNIVERSITY OF WASHINGTON · PI CANGELOSI, GERARD A · 2009 to 2013
$4.7M
NIAID NIH HHS T32 AI007509NIAID NIH HHS U01 AI082186NIAID NIH HHS U01AI082186-01
6 · The paper itself

Abstract

New high-performance detection technologies and more robust protein capture agents can be combined to both rapidly and specifically capture and detect protein biomarkers associated with disease in complex biological samples. Here we demonstrate the use of recently developed recombinant affinity reagents, namely nanoyeast-scFv, in combination with alternating current electrohydrodynamic (ac-EHD)-induced shear forces, to enhance capture performance during protein biomarker analysis. The use of ac-EHD significantly improves fluid transport across the capture domain, resulting in enhanced sensor-target interaction and simultaneous displacement of nonspecific molecules from the electrode surface. We demonstrate this simple proof-of-concept approach for the capture and detection of Entamoeba histolytica antigens from disinfected stool, within a span of 5 min using an ac-EHD microfluidic device. Under an ac-EHD field, antigens were captured on a nanoyeast-scFv immobilized device and subsequently detected using a quantum dot conjugated antibody. This immunosensor specifically detected antigen in disinfected stool with low background noise at concentrations down to 58.8 fM with an interassay reproducibility (%RSD of n = 3) < 17.2%, and in buffer down to 5.88 fM with an interassay reproducibility (% RSD, n = 3) of 8.4%. Furthermore, antigen detection using this immunosensor was 10 times more sensitive than previously obtained with the same nanoyeast-scFv reagents in a microfluidic device employing surface-enhanced Raman scattering (SERS) detection in buffer and at least 200 times more sensitive than methods using screen printed gold electrodes in disinfected stool. We predict this rapid and sensitive approach using these stable affinity reagents may offer a new methodology to detect protein disease biomarkers from biological matrices.

Indexed as

Electrochemical TechniquesHydrodynamicsAntigens, ProtozoanBiomarkersEntamoeba histolyticaSingle-Chain AntibodiesAntigens, ProtozoanBiomarkersSingle-Chain Antibodies

Identifiers

PMID26551436
PMCPMC12486708
OpenAlexW2298096260

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.