Evidence map›Paper›PMID 39235247›Full record

ArticleJournal of clinical microbiology2024

An immunoassay based on bioluminescent sensors for rapid detection of African swine fever virus antibodies.

Zhonghui Zhang, Jinming Wang, Qingli Niu, Guiquan Guan, Hong Yin, Jifei Yang

Abstract readEvaluation Study
In one paragraph

Article in Journal of clinical microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Zhonghui ZhangState Key Laboratory for Animal Disease Control and Prevention, African Swine Fever Regional Laboratory of China (Lanzhou), Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.ORCID 0009-0003-8824-0796
Jinming WangState Key Laboratory for Animal Disease Control and Prevention, African Swine Fever Regional Laboratory of China (Lanzhou), Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Qingli NiuState Key Laboratory for Animal Disease Control and Prevention, African Swine Fever Regional Laboratory of China (Lanzhou), Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.ORCID 0000-0002-4782-5611
Guiquan GuanState Key Laboratory for Animal Disease Control and Prevention, African Swine Fever Regional Laboratory of China (Lanzhou), Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Hong YinState Key Laboratory for Animal Disease Control and Prevention, African Swine Fever Regional Laboratory of China (Lanzhou), Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.
Jifei YangState Key Laboratory for Animal Disease Control and Prevention, African Swine Fever Regional Laboratory of China (Lanzhou), Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu, China.ORCID 0000-0001-7585-4236

Funding

MOST | National Key Research and Development Program of China (NKPs) 2022YFD1800603, 2021YFD1800101, 2021YFD1800500
6 · The paper itself

Abstract

Serological assays for antibody detection have contributed significantly to the diagnosis and control of infectious diseases. African swine fever is the most devastating infectious disease of domestic pigs and wild boars, severely threatening the global pig industry in recent years. Here, we developed a rapid, simple, and sensitive immunoassay based on the split-luciferase system to detect IgG antibodies against African swine fever virus (ASFV). In this assay, the p30 protein of ASFV was genetically coupled to the LgBiT and SmBiT subunits of nanoluciferase, which were used as fusion probes for specific antibodies. Target engagement of the probes results in the reconstitution of a functional nanoluciferase, which further catalyzes bioluminescent reactions. Different orientations of the LgBiT and SmBiT-p30 fusion sensors were designed and investigated, and N-LgBiT/p30 and N-SmBiT/p30 were identified as a promising sensor pair for reforming active nanoluciferase in the presence of specific antibodies. After optimization, this split-luciferase complementation assay showed high sensitivity and specificity for the detection of ASFV antibodies. The analytical sensitivity of the assay was 16 times greater than that of the blocking enzyme-linked immunosorbent assay (ELISA) by the detection of serial dilutions of serum, and no cross-reaction was observed with other swine pathogens. As demonstrated in clinical samples, its performance is highly consistent with that of a commercial ELISA kit, with a concordance rate of 98.19%. This assay is simple and easy to perform, providing a more flexible and efficient approach for the measurement of ASFV antibodies in clinical applications. IMPORTANCE: The study is about a homogeneous split-luciferase assay for antibody detection. Split nanoluciferase biosensors for the detection of ASFV antibodies were designed. This sensor platform enables the sensitive and specific detection of antibodies. The split-luciferase assay is simple, rapid, and easy to use.

Indexed as

African Swine FeverAfrican Swine Fever VirusAntibodies, ViralLuminescent MeasurementsSensitivity and SpecificityAnimalsImmunoassayImmunoglobulin GLuciferasesPhosphoproteinsSwineViral ProteinsAntibodies, ViralImmunoglobulin GLuciferasesP30 protein, African Swine Fever VirusPhosphoproteinsViral ProteinsAfrican swine feverantibodiesluminescent biosensorpoint-of-care testsplit-luciferase complementation assay

Identifiers

PMID39235247
PMCPMC11481549

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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.