Evidence map›Paper›PMID 24964807›Full record

ArticleClinical and vaccine immunology : CVI2014

Application of immunosignatures for diagnosis of valley fever.

Krupa Arun Navalkar, Stephen Albert Johnston, Neal Woodbury, John N Galgiani, D Mitchell Magee, Zbigniew Chicacz, Phillip Stafford

Open access · diamondAbstract read
In one paragraph

Article in Clinical and vaccine immunology : CVI, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

16 citing papers in PubMed, 23 citations in OpenAlex.

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  10. Converging and emerging threats to health security.Environment systems & decisions · 2018
    Review
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  12. General Assessment of Humoral Activity in Healthy Humans.Molecular & cellular proteomics : MCP · 2016
    Article
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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 3 institutions in 1 country.

Krupa Arun NavalkarCenter for Innovations in Medicine, Arizona State University, Tempe, Arizona, USA.
Stephen Albert JohnstonCenter for Innovations in Medicine, Arizona State University, Tempe, Arizona, USA.
Neal WoodburyCenter for Innovations in Medicine, Arizona State University, Tempe, Arizona, USA.
John N GalgianiValley Fever Center for Excellence, University of Arizona, Tucson, Arizona, USA.
D Mitchell MageeCenter for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, Arizona, USA.
Zbigniew ChicaczCenter for Innovations in Medicine, Arizona State University, Tempe, Arizona, USA.
Phillip StaffordCenter for Innovations in Medicine, Arizona State University, Tempe, Arizona, USA phillip.stafford@asu.edu.
Arizona State University · USCenter for Innovation · USUniversity of Arizona · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Valley fever (VF) is difficult to diagnose, partly because the symptoms of VF are confounded with those of other community-acquired pneumonias. Confirmatory diagnostics detect IgM and IgG antibodies against coccidioidal antigens via immunodiffusion (ID). The false-negative rate can be as high as 50% to 70%, with 5% of symptomatic patients never showing detectable antibody levels. In this study, we tested whether the immunosignature diagnostic can resolve VF false negatives. An immunosignature is the pattern of antibody binding to random-sequence peptides on a peptide microarray. A 10,000-peptide microarray was first used to determine whether valley fever patients can be distinguished from 3 other cohorts with similar infections. After determining the VF-specific peptides, a small 96-peptide diagnostic array was created and tested. The performances of the 10,000-peptide array and the 96-peptide diagnostic array were compared to that of the ID diagnostic standard. The 10,000-peptide microarray classified the VF samples from the other 3 infections with 98% accuracy. It also classified VF false-negative patients with 100% sensitivity in a blinded test set versus 28% sensitivity for ID. The immunosignature microarray has potential for simultaneously distinguishing valley fever patients from those with other fungal or bacterial infections. The same 10,000-peptide array can diagnose VF false-negative patients with 100% sensitivity. The smaller 96-peptide diagnostic array was less specific for diagnosing false negatives. We conclude that the performance of the immunosignature diagnostic exceeds that of the existing standard, and the immunosignature can distinguish related infections and might be used in lieu of existing diagnostics.

Indexed as

Antibodies, FungalAntibodies, ViralCoccidioidesCoccidioidomycosisFalse Negative ReactionsHumansImmunoglobulin GImmunoglobulin MProtein Array AnalysisAntibodies, FungalAntibodies, ViralImmunoglobulin GImmunoglobulin M

Identifiers

PMID24964807
PMCPMC4135907
OpenAlexW2043688086

What OpenQuestion holds

Textmetadata
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.