An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling

Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artificial intelligence (AI). It is possible to identify disease-specific biomarkers using real-time and sensitive molecular interactions. These technologies are precious for genetic, epigenetic, and proteomic changes critical to disease progression and treatment response. AI improves multi-omics profiling by analyzing large, diverse data sets and common patterns traditional methods overlook. Machine learning tools Biomarkers Discovery is revolutionizing, drug resistance is being understood, and medicine is being personalized as the combination of AI and nanosensors has advanced the detection of DNA methylation and proteomic signatures and improved our understanding of cancer, cardiovascular disease and vascular disease. Despite these advances, challenges still exist. Difficulties in integrating data sets, retaining sensors, and building scalable computing tools are the biggest obstacles. It also examines various solutions with advanced AI algorithms and innovations, including fabrication in nanosensor design. Moreover, it highlights the potential of nanosensor-assisted, AI-driven multi-omics profiling to revolutionize disease diagnosis and treatment. As technology advances, these tools pave the way for faster diagnosis, more accurate treatment and improved patient outcomes, offering new hope for personalized medicine.

COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset

The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combines convolutional neural networks (CNN) with a bi-directional long short-term memory (Bi-LSTM) network, in conjunction with fiber optic data for SARS-CoV-2 Immunoglobulin G (IgG) antibodies. Our research introduces a comprehensive data preprocessing pipeline and evaluates the performance of four different deep learning (DL) algorithms: CNN, CNN-RNN, BiLSTM, and CNN-BiLSTM, in classifying samples as positive or negative for the COVID-19 virus. Among these, the CNN-BiLSTM classifier demonstrated superior performance on the training datasets, achieving an accuracy of 89 %, a recall of 88 %, a precision of 90 %, an F1-score of 89 %, a specificity of 90 %, a geometric mean (G-mean) of 89 %, and a receiver operating characteristic (ROC) of 96 %. In addition, the achieved classification results were compared with those reported in the literature. The findings indicate that the proposed model has promising potential for classifying COVID-19 and could serve as a valuable tool for healthcare professionals. The use of IgG antibodies to detect the virus enhances the specificity and accuracy of the diagnostic tool.

Evaluation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a high figure-of-merit plasmonic multimode refractive index optical sensor

In recent years, following the outbreak of the COVID-19 pandemic, there has been a significant increase in cases of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) and related deaths worldwide. Despite the pandemic nearing its end due to the introduction of mass-produced vaccines against SARS-CoV-2, early detection and diagnosis of the virus remain crucial in preventing disease progression. This article explores the rapid identification of SARS-CoV-2 by implementing a multimode plasmonic refractive index (MMRI) optical sensor, developed based on the split ring resonator (SRR) design. The Finite Difference Time Domain (FDTD) numerical solution method simulates the sensor. The studied sensor demonstrates three resonance modes within the reflection spectrum ranging from 800 nm to 1400 nm. Its material composition and dimensional parameters are optimized to enhance the sensor's performance. The research indicates that all three resonance modes exhibit strong performance with high sensitivity and figures of merit. Notably, the first mode achieves an exceptional sensitivity of 557 nm/RIU, while the third mode exhibits a commendable sensitivity of 453 nm/RIU and a Figure of Merit (FOM) of 45 RIU-1. These findings suggest that the developed MMRI optical sensor holds significant potential for the early and accurate detection of SARS-CoV-2, contributing to improved disease management and control efforts.

Electrochemical Sensors and Biosensors for Identification of Viruses: A Critical Review

Due to their life cycle, viruses can disrupt the metabolism of their hosts, causing diseases. If we want to disrupt their life cycle, it is necessary to identify their presence. For this purpose, it is possible to use several molecular-biological and bioanalytical methods. The reference selection was performed based on electronic databases (2020-2023). This review focused on electrochemical methods with high sensitivity and selectivity (53% voltammetry/amperometry, 33% impedance, and 12% other methods) which showed their great potential for detecting various viruses. Moreover, the aforementioned electrochemical methods have considerable potential to be applicable for care-point use as they are portable due to their miniaturizability and fast speed analysis (minutes to hours), and are relatively easy to interpret. A total of 2011 articles were found, of which 86 original papers were subsequently evaluated (the majority of which are focused on human pathogens, whereas articles dealing with plant pathogens are in the minority). Thirty-two species of viruses were included in the evaluation. It was found that most of the examined research studies (77%) used nanotechnological modifications. Other ones performed immunological (52%) or genetic analyses (43%) for virus detection. 5% of the reports used peptides to increase the method's sensitivity. When evaluable, 65% of the research studies had LOD values in the order of ng or nM. The vast majority (79%) of the studies represent proof of concept and possibilities with low application potential and a high need of further research experimental work.

COVID-19 Detection Using Contemporary Biosensors and Machine Learning Approach: A Review

The current global pandemic not only claims countless human lives but also rocks the economies of every country on the planet. This fact needs the development of novel, productive, and efficient techniques to detect the SARS-CoV-2 virus. This review article discusses the current state of SARS-CoV-2 virus detection methods such as electrochemical, fluorescent, and electronic, etc., as well as the potential of optical sensors with a wide range of novel approaches and models. This review provides a comprehensive comparison of various detection methods by comparing the various techniques in depth. In addition, there is a brief discussion of the futuristic approach combining optical sensors with machine learning algorithms. It is believed that this study would prove to be critical for the scientific community to explore solutions for detecting viruses with improved functionality.

Label-Free Biochemical Sensing Using Processed Optical Fiber Interferometry: A Review

Over the last 20 years, optical fiber-based devices have been exploited extensively in the field of biochemical sensing, with applications in many specific areas such as the food processing industry, environmental monitoring, health diagnosis, bioengineering, disease diagnosis, and the drug industry due to their compact, label-free, and highly sensitive detection. The selective and accurate detection of biochemicals is an essential part of biosensing devices, which is to be done through effective functionalization of highly specific recognition agents, such as enzymes, DNA, receptors, etc., over the transducing surface. Among many optical fiber-based sensing technologies, optical fiber interferometry-based biosensors are one of the broadly used methods with the advantages of biocompatibility, compact size, high sensitivity, high-resolution sensing, lower detection limits, operating wavelength tunability, etc. This Review provides a comprehensive review of the fundamentals as well as the current advances in developing optical fiber interferometry-based biochemical sensors. In the beginning, a generic biosensor and its several components are introduced, followed by the fundamentals and state-of-art technology behind developing a variety of interferometry-based fiber optic sensors. These include the Mach-Zehnder interferometer, the Michelson interferometer, the Fabry-Perot interferometer, the Sagnac interferometer, and biolayer interferometry (BLI). Further, several technical reports are comprehensively reviewed and compared in a tabulated form for better comparison along with their advantages and disadvantages. Further, the limitations and possible solutions for these sensors are discussed to transform these in-lab devices into commercial industry applications. At the end, in conclusion, comments on the prospects of field development toward the commercialization of sensor technology are also provided. The Review targets a broad range of audiences including beginners and also motivates the experts helping to solve the real issues for developing an industry-oriented sensing device.

Uncovering the morphological differences between SARS-CoV-2 and SARS-CoV based on transmission electron microscopy images

Comprehending the morphological disparities between SARS-CoV-2 and SARS-CoV viruses can shed light on the underlying mechanisms of infection and facilitate the development of effective diagnostic tools and treatments. Hence, this study aimed to conduct a comprehensive analysis and comparative assessment of the morphology of SARS-CoV-2 and SARS-CoV using transmission electron microscopy (TEM) images. The dataset encompassed 519 isolated SARS-CoV-2 images obtained from patients in Italy (INMI) and 248 isolated SARS-CoV images from patients in Germany (Frankfurt). In this paper, we employed TEM images to scrutinize morphological features, and the outcomes were contrasted with those of SARS-CoV viruses. The findings reveal disparities in the characteristics of SARS-CoV-2 and SARS-CoV, such as envelope protein (E) 98.6 and 102.2 nm, length of spike protein (S) 10.11 and 9.50 nm, roundness 0.86 and 0.88, circularity 0.78 and 0.76, and area sizes 25145.54 and 38591.35 pixels, respectively. In conclusion, these results will augment the identification of virus subtypes, aid in the study of antiviral medications, and enhance our understanding of disease progression and the virus life cycle. Moreover, these findings have the potential to assist in the development of more accurate epidemiological prediction models for COVID-19, leading to better outbreak management and saving lives.

A lightweight CORONA-NET for COVID-19 detection in X-ray images

Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors.

Detection of Virus SARS-CoV-2 Using a Surface Plasmon Resonance Device Based on BiFeO3-Graphene Layers

Coronavirus disease (COVID-19) pandemic outbreak is being investigated by severe respirational syndrome coronavirus-2 (SARS-CoV-2) as a global health issue. It is crucial to propose sensitive and rapid coronavirus detectors. Herein, we propose a biosensor based on surface plasmon resonance (SPRE) for the detection of SARS-CoV-2 virus. To achieve improved sensitivity, a BiFeO3 layer is inserted between a metal (Ag) thin film and a graphene layer in the proposed SPRE device so that it has the structure BK7 prism/ Ag/ BiFeO3/ graphene/ analyte. It has been demonstrated that a small variation in the refractive index of the analyte can cause a considerable shift in the resonance angle caused by the remarkable dielectric properties of the BiFeO3 layer, which include a high index of refraction and low loss. The proposed device has shown an extremely high sensitivity of 293 deg/RIU by optimizing the thicknesses of Ag, BiFeO3, and the number of graphene sheets. The proposed SPRE-based sensor is encouraging for use in various sectors of biosensing because of its high sensitivity.

Density estimation of SARS-CoV2 spike proteins using super pixels segmentation technique

The worldwide outbreak of COVID-19 disease was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2). The existence of spike proteins, which allow these viruses to infect host cells, is one of the distinctive biological traits of various prior viruses. As a result, the process by which these viruses infect people is largely dependent on spike proteins. The density of SARS-CoV-2 spike proteins must be estimated to better understand and develop diagnostics and vaccines against the COVID-19 pandemic. CT scans and X-rays have three issues: frosted glass, consolidation, and strange roadway layouts. Each of these issues can be graded separately or together. Although CT scan is sensitive to COVID-19, it is not very specific. Therefore, patients who obtain these results should have more comprehensive clinical and laboratory tests to rule out other probable reasons. This work collected 586 SARS-CoV 2 transmission electron microscopy (TEM) images from open source for density estimation of virus spike proteins through a segmentation approach based on the superpixel technique. As a result, the spike density means of SARS-CoV2 and SARS-CoV were 21,97 nm and 22,45 nm, respectively. Furthermore, in the future, we aim to include this model in an intelligent system to enhance the accuracy of viral detection and classification. Moreover, we can remotely connect hospitals and public sites to conduct environmental hazard assessments and data collection.

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

A label-free electrochemical DNA biosensor used a printed circuit board gold electrode (PCBGE) to detect SARS-CoV-2 without amplification

The emergence of coronavirus disease 2019 (COVID-19) motivates continuous efforts to develop robust and accurate diagnostic tests to detect severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Detection of viral nucleic acids provides the highest sensitivity and selectivity for diagnosing early and asymptomatic infection because the human immune system may not be active at this stage. Therefore, this work aims to develop a label-free electrochemical DNA biosensor for SARS-CoV-2 detection using a printed circuit board-based gold substrate (PCBGE). The developed sensor used the nucleocapsid phosphoprotein (N) gene as a biomarker. The DNA sensor-based PCBGE was fabricated by self-assembling a thiolated single-stranded DNA (ssDNA) probe onto an Au surface, which performed as the working electrode (WE). The Au surface was then treated with 6-mercapto-1-hexanol (MCH) before detecting the target N gene to produce a well-oriented arrangement of the immobilized ssDNA chains. The successful fabrication of the biosensor was characterized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and atomic force microscopy (AFM). The DNA biosensor performances were evaluated using a synthetic SARS-CoV-2 genome and 20 clinical RNA samples from healthy and infected individuals through EIS. The developed DNA biosensor can detect as low as 1 copy per μL of the N gene within 5 minutes with a LOD of 0.50 μM. Interestingly, the proposed DNA sensor could distinguish the expression of SARS-CoV-2 RNA in a patient diagnosed with COVID-19 without any amplification technique. We believe that the proposed DNA sensor platform is a promising point-of-care (POC) device for COVID-19 viral infection since it offers a rapid detection time with a simple design and workflow detection system, as well as an affordable diagnostic assay.

Impedimetric Sensing: An Emerging Tool for Combating the COVID-19 Pandemic

The COVID-19 pandemic revealed a pressing need for the development of sensitive and low-cost point-of-care sensors for disease diagnosis. The current standard of care for COVID-19 is quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). This method is sensitive, but takes time, effort, and requires specialized equipment and reagents to be performed correctly. This make it unsuitable for widespread, rapid testing and causes poor individual and policy decision-making. Rapid antigen tests (RATs) are a widely used alternative that provide results quickly but have low sensitivity and are prone to false negatives, particularly in cases with lower viral burden. Electrochemical sensors have shown much promise in filling this technology gap, and impedance spectroscopy specifically has exciting potential in rapid screening of COVID-19. Due to the data-rich nature of impedance measurements performed at different frequencies, this method lends itself to machine-leaning (ML) algorithms for further data processing. This review summarizes the current state of impedance spectroscopy-based point-of-care sensors for the detection of the SARS-CoV-2 virus. This article also suggests future directions to address the technology's current limitations to move forward in this current pandemic and prepare for future outbreaks.

SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images

The SARS-CoV-2 virus is responsible for the rapid global spread of the COVID-19 disease. As a result, it is critical to understand and collect primary data on the virus, infection epidemiology, and treatment. Despite the speed with which the virus was detected, studies of its cell biology and architecture at the ultrastructural level are still in their infancy. Therefore, we investigated and analyzed the viral morphometry of SARS-CoV-2 to extract important key points of the virus's characteristics. Then, we proposed a prediction model to identify the real virus levels based on the optimization of a full recurrent neural network (RNN) using transmission electron microscopy (TEM) images. Consequently, identification of virus levels depends on the size of the morphometry of the area (width, height, circularity, roundness, aspect ratio, and solidity). The results of our model were an error score of training network performance 3.216 × 10^-11 at 639 epoch, regression of -1.6 × 10^-9, momentum gain (Mu) 1 × 10^-9, and gradient value of 9.6852 × 10^-8, which represent a network with a high ability to predict virus levels. The fully automated system enables virologists to take a high-accuracy approach to virus diagnosis, prevention of mutations, and life cycle and improvement of diagnostic reagents and drugs, adding a point of view to the advancement of medical virology.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Specific intracellular signature of SARS-CoV-2 infection using confocal Raman microscopy

SARS-CoV-2 infection remains spread worldwide and requires a better understanding of virus-host interactions. Here, we analyzed biochemical modifications due to SARS-CoV-2 infection in cells by confocal Raman microscopy. Obtained results were compared with the infection with another RNA virus, the measles virus. Our results have demonstrated a virus-specific Raman molecular signature, reflecting intracellular modification during each infection. Advanced data analysis has been used to distinguish non-infected versus infected cells for two RNA viruses. Further, classification between non-infected and SARS-CoV-2 and measles virus-infected cells yielded an accuracy of 98.9 and 97.2 respectively, with a significant increase of the essential amino-acid tryptophan in SARS-CoV-2-infected cells. These results present proof of concept for the application of Raman spectroscopy to study virus-host interaction and to identify factors that contribute to the efficient SARS-CoV-2 infection and may thus provide novel insights on viral pathogenesis, targets of therapeutic intervention and development of new COVID-19 biomarkers.

SPR Based Biosensing Chip for COVID-19 Diagnosis-A Review

Surface Plasmon Resonance (SPR) techniques are highly accurate in detecting biomolecular like blood group measurement, food adulteration, milk adulteration and recently developing as a rapid detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. In order to validate the clinical diagnosis, Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal swabs has been utilized, which is time consuming and expensive. For fast and accurate detection of the SARS-CoV-2 virus, SPR based biosensing chips are described in this review article. SPR sensors have the potential to be employed for fast, accurate, and portable SARS-CoV-2 virus diagnosis. To combat the SARS-CoV-2 pandemic, there is considerable interest in creating innovative biosensors that are quick, reliable, and sensitive for COVID-19 diagnosis.

SPR Based Biosensing Chip for COVID-19 Diagnosis-A Review

Surface Plasmon Resonance (SPR) techniques are highly accurate in detecting biomolecular like blood group measurement, food adulteration, milk adulteration and recently developing as a rapid detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. In order to validate the clinical diagnosis, Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal swabs has been utilized, which is time consuming and expensive. For fast and accurate detection of the SARS-CoV-2 virus, SPR based biosensing chips are described in this review article. SPR sensors have the potential to be employed for fast, accurate, and portable SARS-CoV-2 virus diagnosis. To combat the SARS-CoV-2 pandemic, there is considerable interest in creating innovative biosensors that are quick, reliable, and sensitive for COVID-19 diagnosis.

Utilizing Electrochemical-Based Sensing Approaches for the Detection of SARS-CoV-2 in Clinical Samples: A Review

The development of precise and efficient diagnostic tools enables early treatment and proper isolation of infected individuals, hence limiting the spread of coronavirus disease 2019 (COVID-19). The standard diagnostic tests used by healthcare workers to diagnose severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection have some limitations, including longer detection time, the need for qualified individuals, and the use of sophisticated bench-top equipment, which limit their use for rapid SARS-CoV-2 assessment. Advances in sensor technology have renewed the interest in electrochemical biosensors miniaturization, which provide improved diagnostic qualities such as rapid response, simplicity of operation, portability, and readiness for on-site screening of infection. This review gives a condensed overview of the current electrochemical sensing platform strategies for SARS-CoV-2 detection in clinical samples. The fundamentals of fabricating electrochemical biosensors, such as the chosen electrode materials, electrochemical transducing techniques, and sensitive biorecognition molecules, are thoroughly discussed in this paper. Furthermore, we summarised electrochemical biosensors detection strategies and their analytical performance on diverse clinical samples, including saliva, blood, and nasopharyngeal swab. Finally, we address the employment of miniaturized electrochemical biosensors integrated with microfluidic technology in viral electrochemical biosensors, emphasizing its potential for on-site diagnostics applications.

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

In order to improve the detection function of wearable intelligent devices in the Internet of things and facilitate people to control a variety of information such as heart rate, exercise state, blood oxygen saturation, and so on, the scientific detection of human physical health based on wearable devices based on Internet of things technology is proposed. Through the combination of software- and hardware-related functional modules, the real-time detection of human physical health information can be effectively realized. Firstly, the detection principle of optical capacitance product pulse wave signal and the waveform characteristics of pulse wave are introduced, and then the application scenarios and advantages of wearable devices are further introduced; then, the convolutional neural network for pulse wave signal denoising and the basic principle of self-encoder are introduced; finally, the regression prediction method and support vector machine method for pulse wave signal feature extraction are introduced in detail. The pulse wave based on optical capacitance product is removed to improve the waveform quality of pulse wave signal. Firstly, the system software development environment is briefly described. Then, the software design of watch terminal master device based on MSP432 and belt terminal slave device based on MSP430 are described in detail, and the detailed program implementation flow of each key technology in the system is given. In addition, the fall detection algorithm based on threshold discrimination is studied, and the program implementation of the algorithm is also described in detail. Finally, the system is tested. The results show that normal state mainly include normal walking, jogging, and fast sitting, and the accuracy rate is 97%, 95%, and 93%, respectively. For fall state, the experimenter needs to simulate various possible fall states, and the accuracy rate is 95%, 93%, and 95%, respectively, which verifies the detection accuracy of the algorithm. The system can automatically turn on the satellite positioning function when the user’s physical sign parameters are abnormal or the user’s current fall dangerous situation occurs, and send the current position information and alarm content information through the GSM module, so that the dangerous situation can be found and handled at the first time.

Photonics enabled intelligence system to identify SARS-CoV 2 mutations

Abstract

The COVID-19, MERS-CoV, and SARS-CoV are hazardous epidemics that have resulted in many deaths which caused a worldwide debate. Despite control efforts, SARS-CoV-2 continues to spread, and the fast spread of this highly infectious illness has posed a grave threat to global health. The effect of the SARS-CoV-2 mutation, on the other hand, has been characterized by worrying variations that modify viral characteristics in response to the changing resistance profile of the human population. The repeated transmission of virus mutation indicates that epidemics are likely to occur. Therefore, an early identification system of ongoing mutations of SARS-CoV-2 will provide essential insights for planning and avoiding future outbreaks. This article discussed the following highlights: First, comparing the omicron mutation with other variants; second, analysis and evaluation of the spread rate of the SARS-CoV 2 variations in the countries; third, identification of mutation areas in spike protein; and fourth, it discussed the photonics approaches enabled with artificial intelligence. Therefore, our goal is to identify the SARS-CoV 2 virus directly without the need for sample preparation or molecular amplification procedures. Furthermore, by connecting through the optical network, the COVID-19 test becomes a component of the Internet of healthcare things to improve precision, service efficiency, and flexibility and provide greater availability for the evaluation of the general population.

Key points

• A proposed framework of photonics based on AI for identifying and sorting SARS-CoV 2 mutations.

• Comparative scatter rates Omicron variant and other SARS-CoV 2 variations per country.

• Evaluating mutation areas in spike protein and AI enabled by photonic technologies for SARS-CoV 2 virus detection.

A Study of the Detection of SARS-CoV-2 ORF1ab Gene by the Use of Electrochemiluminescent Biosensor Based on Dual-Probe Hybridization

To satisfy the need to develop highly sensitive methods for detecting the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and further enhance detection efficiency and capability, a new method was created for detecting SARS-CoV-2 of the open reading frames 1ab (ORF1ab) target gene by a electrochemiluminescence (ECL) biosensor based on dual-probe hybridization through the use of a detection model of "magnetic capture probes-targeted nucleic acids-Ru(bpy)₃²⁺ labeled signal probes". The detection model used magnetic particles coupled with a biotin-labeled complementary nucleic acid sequence of the SARS-CoV-2 ORF1ab target gene as the magnetic capture probes and Ru(bpy)₃²⁺ labeled amino modified another complementary nucleic acid sequence as the signal probes, which combined the advantages of the highly specific dual-probe hybridization and highly sensitive ECL biosensor technology. In the range of 0.1 fM~10 µM, the method made possible rapid and sensitive detection of the ORF1ab gene of the SARS-CoV-2 within 30 min, and the limit of detection (LOD) was 0.1 fM. The method can also meet the analytical requirements for simulated samples such as saliva and urine with the definite advantages of a simple operation without nucleic acid amplification, high sensitivity, reasonable reproducibility, and anti-interference solid abilities, expounding a new way for efficient and sensitive detection of SARS-CoV-2.

A review on corona virus disease 2019 (COVID-19): current progress, clinical features and bioanalytical diagnostic methods

A new epidemic of acute respiratory viral pneumonia was discovered in central China at the end of 2019. The disease was given the name coronavirus disease 2019 (COVID-19), and the virus that caused this disease was known as severe acute respiratory syndrome coronavirus (SARS-CoV-2). So far, diagnostic methods have been focused on (a) human antibody detection, (b) viral antigen detection and (c) viral gene detection, the latter using RT-PCR being the most accurate approach. In this paper, we present a summary of the COVID-19 pandemic, clinical features and epidemiology and pathogenesis. Also, we focus on the recent advances in bioanalytical diagnostic methods based on various techniques for SARS-CoV-2 sensing that have recently been published (2020–2021). Furthermore, we present the mechanisms, advantages and disadvantages of the most common biosensors for COVID-19 detection, which include optical, electrochemical and piezoelectric biosensors as well as wearable and smart nanobiosensors, immunosensors, aptasensors and genosensors.

SARS-CoV-2 Detection Using Optical Fiber Based Sensor Method

The SARS-CoV-2 Coronavirus disease, also known as the COVID-19 pandemic, has engendered the biggest challenge to human life for the last two years. With a rapid increase in the spread of the Omicron variant across the world, and to contain the spread of COVID-19 in general, it is crucial to rapidly identify this viral infection with minimal logistics. To achieve this, a novel plastic optical fiber (POF) U-shaped probe sensing method is presented for accurate detection of SARS-CoV-2, commonly known as the COVID-19 virus, which has the capability to detect new variants such as Omicron. The sample under test can be taken from oropharyngeal or nasopharyngeal via specific POF U-shaped probe with one end that is fed with a laser source while the other end is connected to a photodetector to receive the response and postprocess for decision-making. The study includes detection comparison with two types of POF with diameters of 200 and 500 µm. Results show that detection is better when a smaller-diameter POF is used. It is also seen that the proposed test bed and its envisaged prototype can detect the COVID-19 variants within 15 min of the test. The proposed approach will make the clinical diagnosis faster, cheaper and applicable to patients in remote areas where there are no hospitals or clinical laboratories due to poverty, geographic obstacles, or other factors.

Tuning and Sensitivity Improvement of Bi-Metallic Structure-Based Surface Plasmon Resonance Biosensor with 2-D ε -Tin Selenide Nanosheets

This manuscript aims to analyze the effect of tin selenide (SnSe) on the sensing application of SPR biosensors. Tin selenide is the 2-dimensional transition metal dichalcogenide material. The proposed multilayer structure has a BK7 prism, a bimetallic layer of Au, tin selenide, and a graphene layer. Tin selenide is used to improve the performance parameters of the biosensor. The ε - SnSe nanosheet is placed in between two layers of gold (Au) in the Kretschmann configuration. The proposed configuration has a maximum sensitivity of 214 deg/RIU, 93.81% higher than the conventional sensor. The performance parameters like full width half maximum, detection accuracy, and quality factor have been analyzed. The ε - SnSe material is an air-stable 2-D. The proposed sensor is suitable for the analysis of chemical, medical, and biological analytes.

COVID-19 and MERS: Are their Chest X-ray and Computed Tomography Scanning Signs Related?

Background

COVID-19 is a respiratory infection brought about by SARS-COV-2. Most of the patients contaminated by this pathogen are afflicted by respiratory syndrome with multiple stages ranging from mild upper respiratory involvement to severe dyspnea and acute respiratory distress syndrome cases. Keeping in mind the high sensitivity of computed tomography (CT) scan in detecting abnormalities, it became the number one modality in COVID-19 diagnosis. A wide diversity of CT features can be found in COVID-19 cases, which can be observed before the onset of clinical signs. The review article is aimed to highlight recent discrepancies in CT-scan and chest X-ray (CXR) characteristics between COVID-19 and Middle East Respiratory Syndrome (MERS).

Method

This review study was performed in the literature from the beginning of COVID-19 until the middle of April 2021. For this reason, all relevant works through scientific citation websites such as Google Scholar, PubMed, and Web of Science have been investigated in the mentioned period.

Results

COVID-19 was more reproductive than MERS, while MERS was significantly higher in terms of mortality rate (COVID-19: 2.3% and MERS: 34.4%). Signs of ground-glass opacity (GGO), peripheral consolidation, and GGO accompanying with consolidation are the same signs CXR in both MERS and COVID-19. Indeed, fever, cough, headache, and sore throat are the most symptoms in all studied patients.

Conclusion

Both COVID-19 and MERS have the same imaging signs. The most similar chest CT findings are GGO, peripheral consolidation, and GGO superimposed by consolidation in both studied diseases, and no statistical differences were seen among the mean number of chest CT-scans in MERS and COVID-19 cases.

Advanced high-throughput biosensor-based diagnostic approaches for detection of severe acute respiratory syndrome-coronavirus-2

Given the global Coronavirus disease-2019 (COVID-19) pandemic, the transmission, and mortality rate increased drastically and affected the healthcare, financial sectors, and livelihood of the common man. The use of conventional diagnostic tools like reverse transcription-polymerase chain reaction enabled to screen and detecting the spread at a normal pace that had few limitations embedded into their operation, such as complex operation, slow response time, inaccurate results, single laboratory-based operation, and limited sample processing capacity. Consequently, the biosensors have merits that helped in point of care testing, rapid response, simple operation, and multiplex detection among others. Moreover, other advancements in diagnostic tools provided the ability of multiplexing and multioperation attributes for the detection of severe acute respiratory syndrome-Coronavirus-2 that enabled the high-throughput diagnosis of the viral infection in real samples with faster and accurate results. Further, modifications in their methodology, design and detection strategy facilitated their high-throughput property to help in the effective management of the COVID-19 pandemic.

Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques

Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.

Perspectives on Assembling Coronavirus Spikes on Fiber Optics to Reveal Broadly Recognizing Antibodies and Generate a Universal Coronavirus Detector

In time of COVID-19 biological detection technologies are of crucial relevance. We propose here the use of state of the art optical fiber biosensors to address two aspects of the fight against SARS-CoV-2 and other pandemic human coronaviruses (HCoVs). Fiber optic biosensors functionalized with HCoV spikes could be used to discover broadly neutralizing antibodies (bnAbs) effective against known HCoVs (SARS-CoV, MERS-CoV and SARS-CoV-2) and likely future ones. In turn, identified bnAbs, once immobilized onto fiber optic biosensors, should be capable to detect HCoVs as diagnostic and environmental sensing devices. The therapeutic and preventative value of bnAbs is immense as they can be used for passive immunization and for the educated development of a universal vaccine (active immunization). Hence, HCoV bnAbs represent an extremely important resource for future preparedness against coronavirus-borne pandemics. Furthermore, the assembly of bnAb-based biosensors constitutes an innovative approach to counteract public health threats, as it bears diagnostic competence additional to environmental detection of a range of pandemic strains. This concept can be extended to different pandemic viruses, as well as bio-warfare threats that entail existing, emerging and extinct viruses (e.g., the smallpox-causing Variola virus). We report here the forefront fiber optic biosensor technology that could be implemented to achieve these aims.

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Rapid Detection of Coronavirus (COVID-19) Using Microwave Immunosensor Cavity Resonator

This paper presents a rapid diagnostic device for the detection of the pandemic coronavirus (COVID-19) using a micro-immunosensor cavity resonator. Coronavirus has been declared an international public health crisis, so it is important to design quick diagnostic methods for the detection of infected cases, especially in rural areas, to limit the spread of the virus. Herein, a proof-of-concept is presented for a portable laboratory device for the detection of the SARS-CoV-2 virus using electromagnetic biosensors. This device is a microwave cavity resonator (MCR) composed of a sensor operating at industrial, scientific and medical (ISM) 2.45 GHz inserted in 3D housing. The changes of electrical properties of measured serum samples after passing the sensor surface are presented. The three change parameters of the sensor are resonating frequency value, amplitude and phase of the reflection coefficient |S₁₁|. This immune-sensor offers a portable, rapid and accurate diagnostic method for the SARS-CoV-2 virus, which can enable on-site diagnosis of infection. Medical validation for the device is performed through biostatistical analysis using the ROC (Receiver Operating Characteristic) method. The predictive accuracy of the device is 63.3% and 60.6% for reflection and phase, respectively. The device has advantages of low cost, low size and weight and rapid response. It does need a trained technician to operate it since a software program operates automatically. The device can be used at ports’ quarantine units, hospitals, etc.

A Nonclinical Spectroscopic Approach for Diagnosing Covid-19: A Concise Perspective

With the COVID-19 outbreak, many challenges are posed before the scientific world to curb this pandemic. The diagnostic testing, treatment, and vaccine development for this infection caught the scientific community's immediate attention. Currently, despite the global proliferation of COVID-19 vaccination, the specific treatment for this disease is yet unknown. Meanwhile, COVID-19 detection or diagnosis using polymerase chain reaction (PCR)-based me hods is expensive and less reliable. Moreover, this technique needs much time to furnish the results. Thus, the elaboration of a highly sensitive and fast method of COVID-19 diagnostics is of great importance. The spectroscopic approach is herein suggested as an efficient detection methodology for COVID-19 diagnosis, particularly Raman spectroscopy, infrared spectroscopy, and mass spectrometry.

Towards Fully Integrated Portable Sensing Devices for COVID-19 and Future Global Hazards: Recent Advances, Challenges, and Prospects

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, this fatal disease has been the leading cause of the death of more than 3.9 million people around the world. This tragedy taught us that we should be well-prepared to control the spread of such infectious diseases and prevent future hazards. As a consequence, this pandemic has drawn the attention of many researchers to the development of portable platforms with short hands-on and turnaround time suitable for batch production in urgent pandemic situations such as that of COVID-19. Two main groups of diagnostic assays have been reported for the detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) including nucleic acid-based and protein-based assays. The main focus of this paper is on the latter, which requires a shorter time duration, less skilled technicians, and faces lower contamination. Furthermore, this paper gives an overview of the complementary metal-oxide-semiconductor (CMOS) biosensors, which are potentially useful for implementing point-of-care (PoC) platforms based on such assays. CMOS technology, as a predominant technology for the fabrication of integrated circuits, is a promising candidate for the development of PoC devices by offering the advantages of reliability, accessibility, scalability, low power consumption, and distinct cost.

Comprehensive Review Tapered Optical Fiber Configurations for Sensing Application: Trend and Challenges

Understanding environmental information is necessary for functions correlated with human activities to improve healthcare quality and reduce ecological risk. Tapered optical fibers reduce some limitations of such devices and can be considerably more responsive to fluorescence and absorption properties changes. Data have been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, PubMed, and Google Scholar. In this narrative review, we have summarized and analyzed eight classes of tapered-fiber forms: fiber Bragg grating (FBG), long-period fiber grating (LPFG), Mach-Zehnder interferometer (MZI), photonic crystals fiber (PCF), surface plasmonic resonance (SPR), multi-taper devices, fiber loop ring-down technology, and optical tweezers. We evaluated many issues to make an informed judgement about the viability of employing the best of these methods in optical sensors. The analysis of performance for tapered optical fibers depends on four mean parameters: taper length, sensitivity, wavelength scale, and waist diameter. Finally, we assess the most potent strategy that has the potential for medical and environmental applications.

Molecular detections of coronavirus: current and emerging methodologies

Introduction: Seven coronavirus species have been identified that can infect humans. While human coronavirus infections had been historically associated with only mild respiratory symptoms similar to the common cold, three coronaviruses identified since 2003, Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and SARS-CoV-2, cause life-threatening severe respiratory syndromes. The coronavirus disease 2019 (COVID-19) caused by the highly transmissible SARS-CoV-2 has triggered a worldwide health emergency. Due to the lack of effective drugs and vaccination, rapid and reliable detection is of vital importance to control coronavirus epidemics/pandemics.Area covered: A literature search was performed in Pubmed covering the detections and diagnostics of SARS, MERS and SARS-CoV-2. This review summarized the current knowledge of established and emerging methods for coronavirus detection. The characteristics of different diagnostic approaches were described, and the strengths and weaknesses of each method were analyzed and compared. In addition, future trends in the field of coronavirus detection were also discussed.Expert opinion: Nucleic acid-based RT-PCR is the current golden-standard of coronavirus detection, while immunoassays provide history of coronavirus infection besides diagnostic information. Integrated high-throughput system holds the great potential and is the trend of future detection and diagnosis of virus infection.

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection During the COVID-19 Pandemic: A Systematic Review and Bibliometric Analysis

Background

Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs).

Objective

This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology.

Methods

To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020.

Results

We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease.

Conclusions

Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

Detection of COVID-19 Virus on Surfaces Using Photonics: Challenges and Perspectives

The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers' effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment.

Detection of COVID-19 Virus on Surfaces Using Photonics: Challenges and Perspectives

The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers' effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment.

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

An Overview of Bio-Inspired Intelligent Imprinted Polymers for Virus Determination

The molecular imprinting polymers (MIPs) have shown their potential in various applications including pharmaceuticals, chemical sensing and biosensing, medical diagnosis, and environmental related issues, owing to their artificial selective biomimetic recognition ability. Despite the challenges posed in the imprinting and recognition of biomacromolecules, the use of MIP for the imprinting of large biomolecular oragnism such as viruses is of huge interest because of the necessity of early diagnosis of virus-induced diseases for clinical and point-of-care (POC) purposes. Thus, many fascinating works have been documented in which such synthetic systems undoubtedly explore a variety of potential implementations, from virus elimination, purification, and diagnosis to virus and bacteria-borne disease therapy. This study is focused comprehensively on the fabrication strategies and their usage in many virus-imprinted works that have appeared in the literature. The drawbacks, challenges, and perspectives are also highlighted.

SARS-CoV-2 RNA Detection with Duplex-Specific Nuclease Signal Amplification

The emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a zoonotic pathogen, has led to the outbreak of coronavirus disease 2019 (COVID-19) pandemic and brought serious threats to public health worldwide. The gold standard method for SARS-CoV-2 detection requires both reverse transcription (RT) of the virus RNA to cDNA and then polymerase chain reaction (PCR) for the cDNA amplification, which involves multiple enzymes, multiple reactions and a complicated assay optimization process. Here, we developed a duplex-specific nuclease (DSN)-based signal amplification method for SARS-CoV-2 detection directly from the virus RNA utilizing two specific DNA probes. These specific DNA probes can hybridize to the target RNA at different locations in the nucleocapsid protein gene (N gene) of SARS-CoV-2 to form a DNA/RNA heteroduplex. DSN cleaves the DNA probe to release fluorescence, while leaving the RNA strand intact to be bound to another available probe molecule for further cleavage and fluorescent signal amplification. The optimized DSN amount, incubation temperature and incubation time were investigated in this work. Proof-of-principle SARS-CoV-2 detection was demonstrated with a detection sensitivity of 500 pM virus RNA. This simple, rapid, and direct RNA detection method is expected to provide a complementary method for the detection of viruses mutated at the PCR primer-binding regions for a more precise detection.

Rapid diagnosis of SARS-CoV-2 using potential point-of-care electrochemical immunosensor: Toward the future prospects

Coronavirus disease (COVID-19) is an emerging and highly infectious disease making global public health concern and socio-economic burden. It is caused due to Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2). It has the tendency to spread rapidly through person-to-person. Currently, several molecular diagnostic platforms such as PCR, qRT-PCR, reverse transcription loop-mediated isothermal amplification (RT-LAMP), CRISPR are utilized for the diagnosis of SARS-CoV-2. These conventional techniques are costly, time consuming and require sophisticated instrumentation facility with well trained personnel for testing. Hence, it is tough to provide testing en-masse to the people in developing countries. On the other hand, several serological biosensors such as lateral flow immunosensor, optical, electrochemical, microfluidics integrated electrochemical/fluorescence is currently utilized for the diagnosis of SARS-CoV-2. In current pandemic situation, there is an urgent need of rapid and efficient diagnosis on mass scale of SARS-CoV-2 for early stage detection. Early monitoring of viral infections can help to control and prevent the spreading of infections in large chunk of population. In this review, the SARS-CoV-2 and their biomarkers in biological samples, collection of samples and recently reported potential electrochemical immunosensors for the rapid diagnosis of SARS-CoV-2 are discussed.

Nanobiotechnology as a platform for the diagnosis of COVID-19: a review

A sensitive method for diagnosing co ronavi rus d isease 2019 (COVID-19) is highly required to fight the current and future global health threats due to s evere a cute r espiratory s yndrome c oronavirus 2 (SARS-CoV 2). However, most of the current methods exhibited high false‐negative rates, resulting in patient misdiagnosis and impeding early treatment. Nanoparticles show promising performance and great potential to serve as a platform for diagnosing viral infection in a short time and with high sensitivity. This review highlighted the potential of nanoparticles as platforms for the diagnosis of COVID-19. Nanoparticles such as gold nanoparticles, magnetic nanoparticles, and graphene (G) were applied to detect SARS-CoV 2. They have been used for molecular-based diagnosis methods and serological methods. Nanoparticles improved specificity and shorten the time required for the diagnosis. They may be implemented into small devices that facilitate the self-diagnosis at home or in places such as airports and shops. Nanoparticles-based methods can be used for the analysis of virus-contaminated samples from a patient, surface, and air. The advantages and challenges were discussed to introduce useful information for designing a sensitive, fast, and low-cost diagnostic method. This review aims to present a helpful survey for the lesson learned from handling this outbreak to prepare ourself for future  pandemic.

Smart sensing systems for in-home health status and emotional well-being monitoring during COVID-19

The COVID-19 pandemic has restricted the mobility of the population. The experts propose several solutions in order to decrease the number of patients infected with this new virus by treating and monitoring them within the comfort of their own home. A new direction for the research has been identified including healthcare smart sensing systems which can provide medical diagnoses, surveillance, and treatment partially or totally remotely. The field of wearable, smart sensing solutions is becoming nowadays a widely accepted solution characterized also by the increased level of acceptance with regard to home health status monitoring. Pervasive computing and wearable solutions are frequently a topic included in current projects and are expected in new future developments, particularly in the pandemic context which forces people to remain mostly at home. As part of wearable devices the design of textiles, computer science, and smart materials are the three major development directions. The latest developments associated with the monitoring of health status and emotional well-being are presented and discussed in this chapter.