Quantitative circular flow immunoassays with trained object recognition to detect antibodies to SARS-CoV-2 membrane glycoprotein

Huang, Ryan Yuki; Herr, Deron R.

doi:10.1016/j.bbrc.2021.05.073

Cited by 3 publications

(2 citation statements)

References 32 publications

(34 reference statements)

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“…Although the detection of low concentrations of antibodies by LFIAs could give rise to false-negative or false-positive results compared with ELISA ( La Rosa Fabian and Urquizo Briceno, 2020 ), many techniques have been developed to improve the sensitivity and specificity of the diagnosis ( Hu et al, 2021 ). Recently, deep learning–based LFIAs and multiple-antigen spotted circular flow immunoassays (CFIAs) have been developed in our group ( Huang and Herr, 2021 ). Bacterial lysates are mixtures of antigens derived from inactivated microorganisms.…”

Section: Discussionmentioning

confidence: 99%

Circulating Antibodies to Skin Bacteria Detected by Serological Lateral Flow Immunoassays Differentially Correlated With Bacterial Abundance

Huang¹,

Lee²,

Moochhala³

2021

Front. Microbiol.

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The serological lateral flow immunoassay (LFIA) was used to detect circulating antibodies to skin bacteria. Next-generation sequencing analysis of the skin microbiome revealed a high relative abundance of Cutibacterium acnes but low abundance of Staphylococcus aureus and Corynebacterium aurimucosum on human facial samples. Yet, results from both LFIA and antibody titer quantification in 96-well microplates illustrated antibody titers that were not correspondent, and instead negatively correlated, to their respective abundance with human blood containing higher concentrations of antibodies to both S. aureus and C. aurimucosum than C. acnes. Acne vulgaris develops several unique microbial and cellular features, but its correlation with circulating antibodies to bacteria in the pilosebaceous unit remains unknown. Results here revealed that antibodies to C. acnes and S. aureus were approximately 3-fold higher and 1.5-fold lower, respectively, in acne patients than in healthy subjects. Although the results can be further validated by larger sample sizes, the proof-of-concept study demonstrates a newfound discrepancy between the abundance of skin bacteria and amounts of their corresponding antibodies. And in light of acne-correlated amplified titers of specific anticommensal antibodies, we highlight that profiling these antibodies in the pilosebaceous unit by LFIAs may provide a unique signature for monitoring acne vulgaris.

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Section: Discussionmentioning

confidence: 99%

Circulating Antibodies to Skin Bacteria Detected by Serological Lateral Flow Immunoassays Differentially Correlated With Bacterial Abundance

Huang¹,

Lee²,

Moochhala³

2021

Front. Microbiol.

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“…Huang et al [ 64 ] created a YOLO v4-based object detection method for multiplexed circular-flow immunoassay test strips that can rapidly measure and distinguish antibodies that bind the membrane glycoprotein of severe acute respiratory syndrome coronavirus 2. The lateral flow immunoassays are comprised of many pads, including sample, conjugate, absorbent pads, and nitrocellulose membrane.…”

Section: Deep Learningmentioning

confidence: 99%

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Hasan

Islam

Sadeq

et al. 2023

Sensors

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Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

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Multiplex Technologies in COVID-19 Research, Diagnostics, and Prognostics: Battling the Pandemic

Guest

Zahedipour

Majeed

et al. 2022

Multiplex Biomarker Techniques

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Quantitative circular flow immunoassays with trained object recognition to detect antibodies to SARS-CoV-2 membrane glycoprotein

Cited by 3 publications

References 32 publications

Circulating Antibodies to Skin Bacteria Detected by Serological Lateral Flow Immunoassays Differentially Correlated With Bacterial Abundance

Circulating Antibodies to Skin Bacteria Detected by Serological Lateral Flow Immunoassays Differentially Correlated With Bacterial Abundance

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Multiplex Technologies in COVID-19 Research, Diagnostics, and Prognostics: Battling the Pandemic

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