A Closed-loop Approach to Fight Coronavirus: Early Detection and Subsequent Treatment

Rong, Guoguang; Zheng, Yuqiao; Yang, Xi; Bao, Kangjian; Xia, Fen; Ren, Hongyan; Bian, Sumin; Li, Lan

doi:10.20944/preprints202208.0390.v1

2022

DOI: 10.20944/preprints202208.0390.v1

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A Closed-loop Approach to Fight Coronavirus: Early Detection and Subsequent Treatment

Guoguang Rong¹,

Yuqiao Zheng²,

Xi Yang³

et al.

Abstract: The recent COVID-19 pandemic has caused tremendous damage to social economy and people’s health. Some major issues fighting COVID-19 include early and accurate diagnosis and the shortage of ventilator machines for critical patients. In this work, we propose a closed-loop solution to deal with COVID-19: portable biosensing and wearable photoacoustic imaging for early and accurate diagnosis of infection and magnetically neuromodulation or minimally invasive electrical stimulation to replace the traditi… Show more

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Cited by 3 publications

References 18 publications

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Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Rong,

Xu,

Sawan

2023

Biosensors

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We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.

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Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Rong,

Xu,

Sawan

2023

Biosensors

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Tamm Plasmon Polariton Biosensors Based on Porous Silicon: Design, Validation and Analysis

Rong,

Sawan

2023

Biosensors

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Tamm Plasmon Polariton (TPP) is a nanophotonic phenomenon that has attracted much attention due to its spatial strong field confinement, ease of mode excitation, and polarization independence. TPP has applications in sensing, storage, lasing, perfect absorber, solar cell, nonlinear optics, and many others. In this work, we demonstrate a biosensing platform based on TPP resonant mode. Both theoretical analyses based on the transfer matrix method and experimental validation through nonspecific detection of liquids of different refractive indices and specific detection of SARS-CoV-2 nucleocapsid protein (N-protein) are presented. Results show that the TPP biosensor has high sensitivity and good specificity. For N-protein detection, the sensitivity can be up to 1.5 nm/(µg/mL), and the limit of detection can reach down to 7 ng/mL with a spectrometer of 0.01 nm resolution in wavelength shift. Both nonspecific detection of R.I. liquids and specific detection of N-protein have been simulated and compared with experimental results to demonstrate consistency. This work paves the way for design, optimization, fabrication, characterization, and performance analysis of TPP based biosensors.

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Optical Biosensor Based on Porous Silicon and Tamm Plasmon Polariton for Detection of CagA Antigen of Helicobacter pylori

Rong,

Kavokin,

Sawan

2024

Sensors

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Helicobacter pylori (H. pylori) is a common pathogen with a high prevalence of infection in human populations. The diagnosis of H. pylori infection is critical for its treatment, eradication, and prognosis. Biosensors have been demonstrated to be powerful for the rapid onsite detection of pathogens, particularly for point-of-care test (POCT) scenarios. In this work, we propose a novel optical biosensor, based on nanomaterial porous silicon (PSi) and photonic surface state Tamm Plasmon Polariton (TPP), for the detection of cytotoxin-associated antigen A (CagA) of H. pylori bacterium. We fabricated the PSi TPP biosensor, analyzed its optical characteristics, and demonstrated through experiments, with the sensing of the CagA antigen, that the TPP biosensor has a sensitivity of 100 pm/(ng/mL), a limit of detection of 0.05 ng/mL, and specificity in terms of positive-to-negative ratio that is greater than six. From these performance factors, it can be concluded that the TPP biosensor can serve as an effective tool for the diagnosis of H. pylori infection, either in analytical labs or in POCT applications.

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A Closed-loop Approach to Fight Coronavirus: Early Detection and Subsequent Treatment

Cited by 3 publications

References 18 publications

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Tamm Plasmon Polariton Biosensors Based on Porous Silicon: Design, Validation and Analysis

Optical Biosensor Based on Porous Silicon and Tamm Plasmon Polariton for Detection of CagA Antigen of Helicobacter pylori

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