Evidence map›Paper›PMID 28077698›Full record

ArticleJournal of clinical microbiology2017

Detection of Antigenic Variants of Subtype H3 Swine Influenza A Viruses from Clinical Samples.

Brigitte E Martin, Andrew S Bowman, Lei Li, Jacqueline M Nolting, David R Smith, Larry A Hanson, Xiu-Feng Wan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical microbiology, 2017. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Antigenic characterization of influenza and SARS-CoV-2 viruses.Analytical and bioanalytical chemistry · 2022
    Review
  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.

Brigitte E MartinDepartment of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, USA.
Andrew S BowmanDepartment of Veterinary Preventive Medicine, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-0738-8453
Lei LiDepartment of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, USA.
Jacqueline M NoltingDepartment of Veterinary Preventive Medicine, College of Veterinary Medicine, The Ohio State University, Columbus, Ohio, USA.
David R SmithDepartment of Population Medicine and Pathobiology, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, USA.
Larry A HansonDepartment of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, USA.
Xiu-Feng WanDepartment of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, USA wan@cvm.msstate.edu.

Funding

Genome Based Influenza Vaccine Strain Selection using Machine LearningR01AI116744 · NIAID · UNIVERSITY OF MISSOURI-COLUMBIA · PI WAN, XIUFENG HENRY · 2015 to 2019
$1.9M
NIAID NIH HHS R01 AI116744
6 · The paper itself

Abstract

A large population of genetically and antigenically diverse influenza A viruses (IAVs) are circulating among the swine population, playing an important role in influenza ecology. Swine IAVs not only cause outbreaks among swine but also can be transmitted to humans, causing sporadic infections and even pandemic outbreaks. Antigenic characterizations of swine IAVs are key to understanding the natural history of these viruses in swine and to selecting strains for effective vaccines. However, influenza outbreaks generally spread rapidly among swine, and the conventional methods for antigenic characterization require virus propagation, a time-consuming process that can significantly reduce the effectiveness of vaccination programs. We developed and validated a rapid, sensitive, and robust method, the polyclonal serum-based proximity ligation assay (polyPLA), to identify antigenic variants of subtype H3N2 swine IAVs. This method utilizes oligonucleotide-conjugated polyclonal antibodies and quantifies antibody-antigen binding affinities by quantitative reverse transcription-PCR (RT-PCR). Results showed the assay can rapidly detect H3N2 IAVs directly from nasal wash or nasal swab samples collected from laboratory-challenged animals or during influenza surveillance at county fairs. In addition, polyPLA can accurately separate the viruses at two contemporary swine IAV antigenic clusters (H3N2 swine IAV-α and H3N2 swine IAV-ß) with a sensitivity of 84.9% and a specificity of 100.0%. The polyPLA can be routinely used in surveillance programs to detect antigenic variants of influenza viruses and to select vaccine strains for use in controlling and preventing disease in swine.

Indexed as

Antigenic VariationAnimalsAntigens, ViralImmunoassayInfluenza A Virus, H3N2 SubtypeOrthomyxoviridae InfectionsReal-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionSwineSwine DiseasesVirologyAntigens, Viralantigenic driftantigenic variationapproximate ligation assayclinical sampleH3N2influenza A viruspolyPLAserological assayswine influenza virusvaccine strain selection

Identifiers

PMID28077698
PMCPMC5377830

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.