Dataset Information

Development of a Fully Automated Desktop Analyzer and Ultrahigh Sensitivity Digital Immunoassay for SARS-CoV-2 Nucleocapsid Antigen Detection.

ABSTRACT:

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has had a significant impact on public health and the global economy. Several diagnostic tools are available for the detection of infectious diseases, with reverse transcription-polymerase chain reaction (RT-PCR) testing specifically recommended for viral RNA detection. However, this diagnostic method is costly, complex, and time-consuming. Although it does not have sufficient sensitivity, antigen detection by an immunoassay is an inexpensive and simpler alternative to RT-PCR. Here, we developed an ultrahigh sensitivity digital immunoassay (d-IA) for detecting SARS-CoV-2 nucleocapsid (N) protein as antigens using a fully automated desktop analyzer based on a digital enzyme-linked immunosorbent assay.

Methods

We developed a fully automated d-IA desktop analyzer and measured the viral N protein as an antigen in nasopharyngeal (NP) swabs from patients with coronavirus disease. We studied nasopharyngeal swabs of 159 and 88 patients who were RT-PCR-negative and RT-PCR-positive, respectively.

Results

The limit of detection of SARS-CoV-2 d-IA was 0.0043 pg/mL of N protein. The cutoff value was 0.029 pg/mL, with a negative RT-PCR distribution. The sensitivity of RT-PCR-positive specimens was estimated to be 94.3% (83/88). The assay time was 28 min.

Conclusions

Our d-IA system, which includes a novel fully automated desktop analyzer, enabled detection of the SARS-CoV-2 N-protein with a comparable sensitivity to RT-PCR within 30 min. Thus, d-IA shows potential for SARS-CoV-2 detection across multiple diagnostic centers including small clinics, hospitals, airport quarantines, and clinical laboratories.

SUBMITTER: Chiba R

PROVIDER: S-EPMC9496537 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Development of a Fully Automated Desktop Analyzer and Ultrahigh Sensitivity Digital Immunoassay for SARS-CoV-2 Nucleocapsid Antigen Detection.

Chiba Ryotaro R Miyakawa Kei K Aoki Kotaro K Morikawa Takamitsu J TJ Moriizumi Yoshiki Y Degawa Takuma T Arai Yoshiyuki Y Segawa Osamu O Tanaka Kengo K Tajima Hideji H Arai Susumu S Yoshinaga Hisatoshi H Tsukada Ryohei R Tani Akira A Fuji Haruhito H Sato Akinobu A Ishii Yoshikazu Y Tateda Kazuhiro K Ryo Akihide A Yoshimura Toru T

Biomedicines 20220915 9

<h4>Background</h4>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak has had a significant impact on public health and the global economy. Several diagnostic tools are available for the detection of infectious diseases, with reverse transcription-polymerase chain reaction (RT-PCR) testing specifically recommended for viral RNA detection. However, this diagnostic method is costly, complex, and time-consuming. Although it does not have sufficient sensitivity, antigen detect ...[more]

PMID: 36140390

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Project description:Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

Dataset Information

Development of a Fully Automated Desktop Analyzer and Ultrahigh Sensitivity Digital Immunoassay for SARS-CoV-2 Nucleocapsid Antigen Detection.

Background

Methods

Results

Conclusions

Publications

Development of a Fully Automated Desktop Analyzer and Ultrahigh Sensitivity Digital Immunoassay for SARS-CoV-2 Nucleocapsid Antigen Detection.

Similar Datasets

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