Evidence map›Paper›PMID 36540572›Full record

ArticleChemosensors (Basel, Switzerland)2022

Peptide Linked Diacetylene Amphiphiles for Detection of Epitope Specific Antibodies.

Natalie Tran, Priyanka Shiveshwarkar, Justyn Jaworski

Open access · goldAbstract read
In one paragraph

Article in Chemosensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 63% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

Natalie TranDepartment of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA.ORCID 0000-0003-4309-0141
Priyanka ShiveshwarkarDepartment of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA.
Justyn JaworskiDepartment of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA.
The University of Texas at Arlington · US

Funding

Development of novel liposome-based assays for quantifying anti-glycan antibody levels in serumR15GM135892 · NIGMS · UNIVERSITY OF TEXAS ARLINGTON · PI JAWORSKI, JUSTYN · 2019 to 2019
$373k
NIGMS NIH HHS R15 GM135892
6 · The paper itself

Abstract

Antibodies produced in response to adaptive immunity provide a receptor with multiple sites for binding to a distinct epitope of an antigen. Determining antibody levels to specific antigens has important clinical applications in assessing immune status or deficiency, monitoring infectious or autoimmune diseases, and diagnosing allergies. Leveraging that a specific antibody will bind to a distinct small peptide epitope without requiring the entire antigen to be present, we demonstrate in this work a proof-of-concept assay to detect the presence of an antibody by using peptide epitopes linked to an amphiphile to generate a vesicle-based sensing system. By affording multiple copies of the epitope site on the vesicle, we revealed that the vesicles visibly aggregate in response to an antibody specific for that epitope due to multivalent binding provided by the antibody. We also uncovered the role of peptide surface density in providing accessible epitopes on the vesicles for antibody binding. In summary, using a peptide derived from the coat protein of human influenza virus directly linked to a diacetylene-containing amphiphile afforded peptide-laden vesicles that proved capable of detecting the presence of antibodies specific for human influenza hemagglutinin.

Indexed as

amphiphileantibodydetectionepitopepeptide

Identifiers

PMID36540572
PMCPMC9762857
OpenAlexW4210696299

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.