Evidence map›Paper›PMID 33900180›Full record

ArticleEmerging infectious diseases2021

Engineered NS1 for Sensitive, Specific Zika Virus Diagnosis from Patient Serology.

Thai Leong Yap, Shin Yee Hong, Jun Hui Soh, Lekha Ravichandraprabhu, Vanessa W X Lim, Hsi-Min Chan, Tommy Z X Ong, Ying Ping Chua, Shi En Koh, Huajing Wang and 3 more

Open access · diamondAbstract read
In one paragraph

Article in Emerging infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 14 citations in OpenAlex.

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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

13 authors at 4 institutions in 1 country.

Thai Leong Yap
Shin Yee Hong
Jun Hui Soh
Lekha Ravichandraprabhu
Vanessa W X Lim
Hsi-Min Chan
Tommy Z X Ong
Ying Ping Chua
Shi En Koh
Huajing Wang
Yee Sin Leo
Jackie Y Ying
William Sun
Experimental Drug Development Centre · SGInstitute of Bioengineering and Nanotechnology · SGNational University of Singapore · SGNational Centre for Infectious Diseases · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dengue virus (DENV) and Zika virus (ZIKV) belong to the Flaviviridae family of viruses spread by Aedes aegypti mosquitoes in tropical and subtropical areas. Accurate diagnostic tests to differentiate the 2 infections are necessary for patient management and disease control. Using characterized ZIKV and DENV patient plasma in a blind manner, we validated an ELISA and a rapid immunochromatographic test for ZIKV detection. We engineered the ZIKV nonstructural protein 1 (NS1) for sensitive serologic detection with low cross reactivity against dengue and developed monoclonal antibodies specific for the ZIKV NS1 antigen. As expected, the serologic assays performed better with convalescent than acute plasma samples; the sensitivity ranged from 71% to 88%, depending on the performance of individual tests (IgM/IgG/NS1). Although serologic tests were generally less sensitive with acute samples, our ZIKV NS1 antibodies were able to complement the serologic tests to achieve greater sensitivity for detecting early infections.

Indexed as

DengueDengue VirusZika VirusZika Virus InfectionAntibodies, ViralEnzyme-Linked Immunosorbent AssayHumansSensitivity and SpecificitySerologic TestsViral Nonstructural ProteinsAntibodies, ViralViral Nonstructural Proteinsdenguediagnosticsenzyme-linked immunosorbent assayflavivirusimmunochromatographyimmunoglobulinmicrocephalymonoclonal antibodyserologic testvirusesZikaZika virus

Identifiers

PMID33900180
PMCPMC8084482
OpenAlexW3157495987

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.