Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody

Tong, Haoyang; Cao, Chaoyu; You, Minli; Han, Shuang; Liu, Zhe; Xiao, Ying; He, Wanghong; Liu, Chang; Peng, Ping; Xue, Zhenrui; Gong, Yan; Chen, Yao; Xu, Feng

doi:10.1016/j.bios.2022.114449

Cited by 66 publications

(52 citation statements)

References 41 publications

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“…However, the availability of those monoclonal antibodies is limited, and it should be noted that pre-exposure and post-exposure prophylaxis is not a substitute for vaccination. Last but not least, artificial intelligence has been shown to be an emerging and promising technology for detecting early COVID-19 infection and monitoring the state of affected individuals [110] as well as a powerful tool for low-cost, fast and large-scale SARS-CoV-2 vaccine effectiveness evaluation [111].…”

Section: Discussionmentioning

confidence: 99%

Solid Organ Rejection following SARS-CoV-2 Vaccination or COVID-19 Infection: A Systematic Review and Meta-Analysis

et al. 2022

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Background: Solid organ rejection post-SARS-CoV-2 vaccination or COVID-19 infection is extremely rare but can occur. T-cell recognition of antigen is the primary and central event that leads to the cascade of events that result in rejection of a transplanted organ. Objectives: To describe the results of a systematic review for solid organ rejections following SARS-CoV-2 vaccination or COVID-19 infection. Methods: For this systematic review and meta-analysis, we searched Proquest, Medline, Embase, Pubmed, CINAHL, Wiley online library, Scopus and Nature through the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines for studies on the incidence of solid organ rejection post-SARS-CoV-2 vaccination or COVID-19 infection, published from 1 December 2019 to 31 May 2022, with English language restriction. Results: One hundred thirty-six cases from fifty-two articles were included in the qualitative synthesis of this systematic review (56 solid organs rejected post-SARS-CoV-2 vaccination and 40 solid organs rejected following COVID-19 infection). Cornea rejection (44 cases) was the most frequent organ observed post-SARS-CoV-2 vaccination and following COVID-19 infection, followed by kidney rejection (36 cases), liver rejection (12 cases), lung rejection (2 cases), heart rejection (1 case) and pancreas rejection (1 case). The median or mean patient age ranged from 23 to 94 years across the studies. The majority of the patients were male (n = 51, 53.1%) and were of White (Caucasian) (n = 51, 53.7%) and Hispanic (n = 15, 15.8%) ethnicity. A total of fifty-six solid organ rejections were reported post-SARS-CoV-2 vaccination [Pfizer-BioNTech (n = 31), Moderna (n = 14), Oxford Uni-AstraZeneca (n = 10) and Sinovac-CoronaVac (n = 1)]. The median time from SARS-CoV-2 vaccination to organ rejection was 13.5 h (IQR, 3.2–17.2), while the median time from COVID-19 infection to organ rejection was 14 h (IQR, 5–21). Most patients were easily treated without any serious complications, recovered and did not require long-term allograft rejection therapy [graft success (n = 70, 85.4%), graft failure (n = 12, 14.6%), survived (n = 90, 95.7%) and died (n = 4, 4.3%)]. Conclusion: The reported evidence of solid organ rejections post-SARS-CoV-2 vaccination or COIVD-19 infection should not discourage vaccination against this worldwide pandemic. The number of reported cases is relatively small in relation to the hundreds of millions of vaccinations that have occurred, and the protective benefits offered by SARS-CoV-2 vaccination far outweigh the risks.

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Section: Discussionmentioning

confidence: 99%

Solid Organ Rejection following SARS-CoV-2 Vaccination or COVID-19 Infection: A Systematic Review and Meta-Analysis

et al. 2022

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“…As a result of quantitative analysis using a smartphone-based reader applied with an artificial intelligence (AI) algorithm, 160 ng/mL LOD and a 625–10,000 ng/mL detection range were obtained, which was superior to the result using gold-nanoparticle-based LFIA. In this study, the AI algorithm was used to accurately identify the test line and the control line and convert the intensity of the detection region to the concentration of the target analyte [ 69 ]. As another example, Hung et al constructed an antibody-measurement-based COVID-19 diagnostic system by combining LFIA using gold colloid as a reporter and a spectrometer for quantitative measurement of test line intensity.…”

Section: Readers For Quantitative Covid-19 Analysismentioning

confidence: 99%

Lateral Flow Immunoassay Reader Technologies for Quantitative Point-of-Care Testing

Park

2022

Sensors

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Due to the recent pandemic caused by coronavirus disease 2019 (COVID-19), the lateral flow immunoassay used for its rapid antigen test is more popular than ever before. However, the history of the lateral flow immunoassay is about 60 years old, and its original purpose of use, such as a COVID-19 rapid antigen test or a pregnancy test, was the qualitative detection of a target analyte. Recently, the demand for quantitative analysis of lateral flow immunoassays is increasing in various fields. Lateral flow immunoassays for quantitative detection using various materials and sensor technologies are being introduced, and readers for analyzing them are being developed. Quantitative analysis readers are highly anticipated for their future development in line with technological advancements such as optical, magnetic field, photothermal, and electrochemical sensors and trends such as weight reduction, miniaturization, and cost reduction of systems. In addition, the sensing, processing, and communication functions of portable personal devices such as smartphones can be used as tools for the quantitative analysis of lateral flow immunoassays. As a result, lateral flow immunoassays can efficiently achieve the goal of rapid diagnosis by point-of-care testing. Readers used for the quantification of lateral flow immunoassays were classified according to the adopted sensor technology, and the research trends in each were reviewed in this paper. The development of a quantitative analysis system was often carried out in the assay aspect, so not only the readers but also the assay development cases were reviewed if necessary. In addition, systems for quantitative analysis of COVID-19, which have recently been gaining importance, were introduced as a separate section.

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“…An iterative rank assignment RReliefF algorithm was invoked for the selection of strongly correlated statistical features from the colorimetric output. [43][44][45][46][47][48] Towards that, the algorithm first selected the possible correlated features from the entire set of extracted features and subsequently monitored the continuous changes of the dependent variables (i.e. plasma glucose concentration) after assigning weightages to these features identified as primary variables.…”

Section: Standardizing the Image Analyticsmentioning

confidence: 99%

Instrument-free single-step direct estimation of the plasma glucose level from one drop of blood using smartphone-interfaced analytics on a paper strip

et al. 2022

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Artificial intelligence-assisted colorimetric lateral flow immunoassay for sensitive and quantitative detection of COVID-19 neutralizing antibody

Cited by 66 publications

References 41 publications

Solid Organ Rejection following SARS-CoV-2 Vaccination or COVID-19 Infection: A Systematic Review and Meta-Analysis

Solid Organ Rejection following SARS-CoV-2 Vaccination or COVID-19 Infection: A Systematic Review and Meta-Analysis

Lateral Flow Immunoassay Reader Technologies for Quantitative Point-of-Care Testing

Instrument-free single-step direct estimation of the plasma glucose level from one drop of blood using smartphone-interfaced analytics on a paper strip

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