Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Dual-comb biosensing of SARS-CoV-2 nucleocapsid protein antigen

One interesting feature of optical frequency comb (OFC) is a function of frequency conversion between region and electric regions. While such feature has been used for generation of correct electric signal in microwave or millimeter region, it can be further used for fiber biosensing;namely, biosensing OFC. In this paper, we demonstrated detection of SARS-CoV-2 antigen based on a combination of dual fiber combs, an intracavity multi-mode-interference fiber sensor, and sensor surface modification of SARS-CoV-2 antibody. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Functional Nanomaterials for Electrochemical SRAS-CoV-2 Biosensors: a Review

The pandemic outbreak of COVID-19 has posed a threat to public health globally, and rapid and accurate identification of the viruses is crucial for controlling COVID-19. In recent years, nanomaterial-based electrochemical sensing techniques hold immense potential for molecular diagnosis with high sensitivity and specificity. In this review, we briefly introduced the structural characteristics and routine detection methods of SARS-CoV-2, then summarized the associated properties and mechanisms of the electrochemical biosensing methods. On the above basis, the research progress of electrochemical biosensors based on gold nanomaterials, oxide nanomaterials, carbon-based nanomaterials and other nanomaterials for rapid and accurate detection of virus were reviewed. Finally, the future applications of nanomaterial-based biosensors for biomolecular diagnostics were pointed out. © 2023 Science Press. All rights reserved.

Enhanced electrochemiluminescence immunoassay: 2. Enabling signal detection at an early stage of incubation for rapid point-of-care testing

Signal detection in a label-based immunoassay is performed normally when the antigen/antibody binding reaction reaches the equilibrium state during the incubation period of an assay process. Shortening the incubation period in an assay helps reduce the turnaround time and is particularly valuable for point-of-care testing, but the cost is the reduction of signal level and, possibly, measurement precision as well. This work demonstrates that the signal loss could be offset by the stronger emission of an electronically neutral ruthenium(II) complex label, Ru(2, 2′-bipyridine) (bathophenanthroline disulfonate)[4-(2, 2′-bipyridin-4-yl)butanoic acid], used in the electrochemiluminescence (ECL) immunoassay. Combined with the uniquely well-established flow-through washing process in the automated ECL analyzers and the precise control over liquid handling, the assays performed with a 5-minute incubation period showed the same signal level and measurement precision as those of conventional ECL assays. Additionally, the absence of biotin and streptavidin components in the reagent formulation avoids the biotin-streptavidin interaction during assay incubation and fundamentally eliminates the interference of biotin, especially when used in some high-dose therapies. The results obtained from the procalcitonin prototype kit and the supporting evidence from other preliminary reagents (for SARS-CoV-2 N protein and troponin T) are general. The nonequilibrium detection, along with the downsized instrument design, makes the enhanced ECL (EECL) technology a fast high-performance POCT platform that provides the same high-quality data as those generated from the widely deployed [Ru(bpy)3]2+ based laboratorial ECL systems. The anticipated regulatory approval and follow-up clinical implementation will be a significant stride in the decade-long pursuit of novel ECL labels. © 2023 The Author(s)

A Review on the Application of Nanomaterials in Virus Detection

R apid, sensitive and specific detection of viruses is a key issue in the medical field. Since 2020, the global outbreak of COVID-19 requires more sensitive virus detection methods. With the development of new materials, especially nanomaterials, many materials have demonstrated great physical, chemical and mechanical properties, which present potential for virus detection. Nanomaterials can be divided into zero-dimensional materials, one-dimensional materials and two-dimensional materials by structure. In this paper, the classification and the latest progress of nanomaterials are reviewed, highlighting their applications in the field of virus detection. The future prospect of nanomaterials in virus detection is also presented and discussed. © 2023 Cailiao Daobaoshe/ Materials Review. All rights reserved.

A review of plastic pollution and their treatment technology: A circular economy platform by thermochemical pathway

The rapid expansion of plastic manufacturing industries in last several decades has brought serious concerns over the environmental impacts of plastic wastes. Recent outbreak of Covid-19 drastically increased production, use, and disposal of plastic products. Current management strategies for wasted plastics still rely on landfill and incineration that continue to exacerbate plastic pollution and carbon emissions. Many countries have put forward multifaceted administrative efforts to reduce plastic wastes, but the annual global generation of plastic wastes is still increasing. In techno-society, researchers have been exploring more effective plastic wastes treatment technologies to alleviate environmental impacts of plastic wastes. Such efforts entailed several technical options that can potentially contribute to establishing a circular economy for plastics. Thermochemical process is a prominent example of such techniques. This review presents an overview of the issue of plastic pollution, covering topics including global plastic production, environmental impacts, and toxicity. In addition, the global administrative efforts aimed at reducing plastic pollution are discussed, as well as detection and treatment strategies to establish a circular economy in plastic management. © 2023 Elsevier B.V.

Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set. © 2023 IEEE.

A Microfluidic Biosensor for Rapid Detection of Covid-19

We have designed, fabricated, and tested a MEMS-based impedance biosensor for accurate and rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) using of clinical samples. The device consists of focusing region that concentrate low quantities of the virus present in the samples to a detectable threshold, trap region hat maximize the captured virus, and detection region to detect the virus with high selectivity and sensitivity, using an array of interdigitated electrodes (IDE) coated with a specific antibody. Changes in the impedance value due to the binding of the SARS-COV-2 antigen to the antibody will indicate positive or negative result. The device was able to detect inactivated SARS-COV-2 antigen present in phosphate buffer saline (PBS) with a concentration as low as 50 TCID50/ml in 30 minutes. In addition, the biosensor was able to detect SARS-COV-2 in clinical samples (swabs) with a sensitivity of 84 TCID50/ml, also in 30 minutes. © 2023 IEEE.

Functional Nanomaterials for Electrochemical SRAS-CoV-2 Biosensors: a Review

The pandemic outbreak of COVID-19 has posed a threat to public health globally, and rapid and accurate identification of the viruses is crucial for controlling COVID-19. In recent years, nanomaterial-based electrochemical sensing techniques hold immense potential for molecular diagnosis with high sensitivity and specificity. In this review, we briefly introduced the structural characteristics and routine detection methods of SARS-CoV-2, then summarized the associated properties and mechanisms of the electrochemical biosensing methods. On the above basis, the research progress of electrochemical biosensors based on gold nanomaterials, oxide nanomaterials, carbon-based nanomaterials and other nanomaterials for rapid and accurate detection of virus were reviewed. Finally, the future applications of nanomaterial-based biosensors for biomolecular diagnostics were pointed out. © 2023 Science Press. All rights reserved.

A portable, low-cost and high-throughput electrochemical impedance spectroscopy device for point-of-care biomarker detection

This paper presents a portable, fast and accurate electrochemical impedance spectroscopy (EIS) device with 8-well interdigitated electrode chips for biomarker detection. The design adopts low crest factor multisine signal synthesis at low frequencies (<1 kHz) and single-tone signals at high frequencies (>1 kHz), which significantly increases measurement speed without sacrificing accuracy. In addition, the low excitation amplitude of 10 mV preserves impedance linearity and protects the biosamples. The system achieved an average magnitude accuracy error of 0.30% in the frequency range of interest and it requires only 0.46 s to scan 28 frequency points from 10 Hz to 1 MHz. Experiments were conducted to test the capability to detect antibodies against SARS-CoV-2. Gold nanoparticles bound with protein G (GNP-G) were employed as the conjugated secondary antibody probe to detect anti-SARS-CoV-2 IgG in serum. A highly statistical significance (p = 7×10−6) could be found in the impedance data at 10 kHz. The impedance magnitude alteration caused by the GNP-G of the positive and negative groups were 27.2%±13.6% and 4.1%±1.7%, respectively. The results imply that the proposed system enables rapid COVID-19 antibody biomarker detection. Moreover, the EIS system and GNPs have the potential to be modified to detect other biomarkers. © 2022 The Author(s)

An ultra-sensitive luteolin sensor based on Co-doped nitrogen-containing carbon framework/MoS2−MWCNTs composite for natural sample detection

As a common antioxidant and antimicrobial agent in plants, luteolin has a variety of pharmacological activities and biological effects, the ability to specifically bind proteins and thus inhibit novel coronaviruses and treat asthma. Here, Co doped nitrogen-containing carbon frameworks/MoS2−MWCNTs (Co@NCF/MoS2−MWCNTs) nanocomposites have been synthesized and successfully applied to electrochemical sensors. X-ray photoelectron spectroscopy, scanning electron microscopy and X-ray diffraction were used to examine the morphology and structure of the samples. Meanwhile, the electrochemical behavior of Co@NCF/MoS2−MWCNTs was investigated. Due to its excellent electrical conductivity, electrocatalytic activity and adsorption, it is used for the detection of luteolin. The Co@NCF/MoS2−MWCNTs/GCE sensor can detect luteolin in a linear range from 0.1 nM to 1.3 μM with a limit of detection of 0.071 nM. Satisfactory results were obtained for the detection of luteolin in natural samples. In addition, the redox mechanism and electrochemical reaction sites of luteolin were investigated by the scan rate of CV curves and density functional theory. This work demonstrates for the first time the combination of ZIF-67-derived Co@NCF and MoS2−MWCNTs as electrochemical sensors for the detection of luteolin, which opens a new window for the sensitive detection of luteolin. © 2022 Elsevier Ltd

Label-free detection of SARS-CoV-2 neutralizing antibodies in human serum by Bloch surface waves on 1D photonic crystal biochips

We propose a rapid serologic test based on disposable nano-photonic biochips for SARS-CoV-2 related antibodies. The label-free sensograms showed that positive and negative human serum samples were discriminated, enabling real-time and fast label-free detection. © 2022 The Author (s)

Label-free detection of SARS-CoV-2 neutralizing antibodies in human serum by Bloch surface waves on 1D photonic crystal biochips

We propose a rapid serologic test based on disposable nano-photonic biochips for SARS-CoV-2 related antibodies. The label-free sensograms showed that positive and negative human serum samples were discriminated, enabling real-time and fast label-free detection. © 2022 The Author (s)

Rapid SARS-CoV-2 S-protein Detection using Nanostructured Electrochemical Biosensor

We have developed a new type of testing strategy based on the electrochemical biosensing aspect for rapid and portable detection of SARS-CoV-2. The detection platform is based on a highly conductive matrix (fabricated polystyrene/polyaniline-Au nanocomposite) enabling immobilization of representative receptor elements (antibodies) that are specific to the target, i.e., SARS-CoV-2 spike (S)-protein. The concept of a detection system is to translate specific covalent interaction between antibodies and its corresponding binding viral S-protein, into a measurable, concentration-dependent electrochemical signal. The biosensor is able to monitor the electrochemical response in PBS, without using hazardous [Fe(CN)]63-/4- redox couple. By creating an electrochemical readout (CV, EIS, and DPV), data enables qualitative and quantitative analysis. Additionally, it exploits outstanding conductivity and biocompatibility, thus resulting in high analytical sensitivity and a low detection limit of 15.6μ g/mL, which is within the physiologically relevant concentration range. Thus, the proposed feasible design of the biosensor platform represents an excellent starting point for practical and low-cost testing of asymptomatic patients or people before symptom onset. © 2022 IEEE.

Detection of Antibodies for COVID-19 from Reflectance Spectrum Using Supervised Machine Learning

Since the coronavirus disease 2019 occurred, the lateral flow immunoassay (LFIA) test strip has become a global testing tool for convenience and low cost. However, some studies have shown that LFIA strips perform poorly compared to other professional testing methods. This paper proposes a new method to improve the accuracy of LFIA strips using spectral signals. A spectrochip module is applied to disperse the reflected light from the LFIA strips. The obtained spectral signals will be used for supervised machine learning. After training, the trained model has 93.8% accuracy compared to the standard test. This result indicated that the evaluation method based on the spectrum of LFIA strips could enhance the detection performance. © 2022 IEEE.

Solving the problem of antibody grouping based on cross-inhibition index using hierarchical clustering methods

Due to increasing number of viral diseases (including Covid-19) rapid research with the purpose of their detection, prevention, and treatment is crucial. This article considers a problem of finding two optimal antibodies to any virus that is important for detection of disease and development of tests but not for creation of vaccine. It is worth noting that the target protein (nucleoprotein), described in this article, is the only generally established target for SARSCoV-2 diagnostics, using antigen rapid tests or any other antigen detection tools. Possible ways of solving the aforementioned problem were described using hierarchical clustering algorithm with different linkage methods. Affirmative results of dividing antibodies into groups were achieved. © 2022 Copyright for this paper by its authors.

Research Progress of Surface Plasmon Resonance and Local Surface Plasmon Resonance in Virus Detection

Significance In 2009, influenza A (H1N1) broke out in Mexico and the United States, influencing 214 countries and killing at least 14000 people. The novel coronavirus epidemic which broke out in 2020 has still been raging all over the world for two years as the results of the huge difficulty in the rapid and real-time epidemic prevention detection and the other reasons. In addition, the spread of other viruses including dengue virus (DENV) and human immunodeficiency virus (HIV) is also threatening human health significantly. Virus detection is the key to curb the spread of the viruses. At present, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), as the gold standard in the field of virus detection, can be used to detect and trace virus samples with a high sensitivity. But these samples need to be collected to the laboratory, and the viruses must be isolated and determined using the sophisticated lab equipment operated by professionals in order to get accurate results. Surface plasmon resonance (SPR)and local surface plasmon resonance (LSPR) biosensors may be an effective alternative, as their structures are simple and easy to be miniaturized. Especially, the LSPR-based device only needs a light source and some sensing elements. Once the sensing elements successfully capture the virus, the detection process will be quickly, sensitively, and selectively finished. These characteristics of the SPR and LSPR techniques show their great application potential in the field of virus detection, especially for the point-of-care testing with limited conditions. With the rapid development of SPR and LSPR-based virus detection researches, researchers have reviewed the progress of materials and structures of sensors, methods for plasmonic virus detection, and their characteristics of signal amplification, and so on. According to the four general virus detection methods and starting from the four kinds of target analytes captured by the sensor, this paper systematically outlines the latest researches of the SPR and LSPR techniques for detecting viruses, which are of great significance for their clinical application (Fig. 1). Progress First, according to the four methods for virus detection, the application progress of SPR and LSPR in the fields of antibody, antigen, nucleic acid, and virus particle detection is reviewed successively. For the SPR or LSPR sensors based on the binding principle of specific antigen-antibodies, the detection limit is further optimized by modifying the appropriate antigens or antibodies. More stable and inexpensive aptamers and molecularly imprinted polymers are expected to replace antibodies as sensor recognition elements to detect virus antigens or particles. Because the number of virus genomes in clinical samples is usually very small, the detection of nucleic acid by SPR or LSPR alone is limited. However, the detection of virus samples with the concentration at the femto scale can be realized by combining SPR or LSPR with DNA amplification and fluorescent substances. Second, the problems of biological medium contamination and repeatability encountered by biosensors as well as their solutions are introduced (Fig. 13). As for the contamination of biological media, self-assembled monolayers (SAM) can be synthesized on the surface of sensor elements to alleviate this problem. Riedel et al. further reduced or even completely inhibited the biological contamination of plasma and serum by synthesizing polymer brushes. In order to ensure the repeatability of sensing elements, Yoo et al. used magnetic beads replaced under the control of magnetic field as the sensing element, allowing that the sensor chip could still work stably after many repeated measurements. Third, the configurations and parameters of the SPR and LSPR sensors for virus detection in the past 15 years are listed (Table 1), and the advantages of the SPR and LSPR techniques are described. Finally, the optimization strategies of the SPR and LSPR techniques and the present existing problems are summarized. Moreover, e application prospect is also forecasted. Conclusion and Prospect According to the current research progress, the optimization strategy of the SPR sensor mainly focuses on film material sensitization and metal particle coupling sensitization. The former includes the application of 2D materials and molecular imprinting through the construction of surface films to enhance practicality and applicability. In contrast, the latter uses nanoparticles to form sandwich structures. The LSPR sensing strategies are concentrated on the design and optimization of nanoparticles or nanostructures, which are often combined with fluorescent substances such as quantum dots (QDs) to form sensing probes for virus detection by the light absorption peak shift or the fluorescence intensity change. The LSPR biosensors are normally easier to be miniaturized than the SPR counterparts. In a word, the SPR and LSPR sensors show great application prospects in the field of virus detection. Predictably, owing to the diversity of the SPR and LSPR virus sensor modifiers, it may be possible to detect specific viruses for multiple target analytes at the same time through the integration of sensor recognition elements, which enables the multi-dimensional evaluation of virus infection in a short time to avoid false negative and false positive cases. © 2022 Science Press. All rights reserved.

Application of nucleic acid aptamer-based lateral flow assay in microbe detection

Microbial contamination and infection are global issues in the food and environmental fields that seriously threaten human health. Bacteria and fungi can easily cause food spoilage, resulting in diarrhea and vomiting;viruses can infect humans through different transmission routes, causing severe or even fatal harm. Hence, rapid analysis and identification of pathogenic microorganisms and simultaneous detection of multiple types of microbes have become hot research topics in biochemical analysis and molecular diagnosis. The lateral flow assay (LFA) is a simple, rapid, economical, and efficient detection technology with high sensitivity, simple operation, and environmental friendliness. It can provide instant test results under non-laboratory circumstances, hence becoming an ideal choice for point-of-care testing, which has been applied to rapidly detect various targets. The current conventional principle of the LFA is still based on the specific recognition of the antigen by the antibody. However, as a commonly used target recognition molecule in conventional biochemical and medical detection, the application of antibodies also has certain limitations for rapid and accurate identification of certain targets due to strict control of the production and purification process, as well as susceptibility to the interference of the operating environment, pH, temperature, and other conditions, such as long production cycle, high cost, poor stability, and cross-reactivity. Aptamers are a class of single-stranded DNA (ssDNA) or RNA obtained through the systematic evolution of ligands by exponential enrichment (SELEX), which can usually form a stable secondary structure. Aptamers can be folded into a three-dimensional structure through conformational change and interact with the target through conformation complementarity, π-π stacking between aromatic rings, base stacking, electrostatic interaction, and hydrogen bonding. So far, nearly 300 kinds of aptamers have been discovered. As alternatives, aptamers are easy and facile to modify and label with high sensitivity and specificity. Accordingly, the innovative rapid detection technique can be developed by combining the LFA with an aptamer. This aptamer-based LFA technology can be widely used in qualitative, semi-quantitative, and quantitative detection in food safety, environment, clinical, and other fields. Nowadays, most microbe detection methods are constructed based on this approach. The common strategies of aptamer-based LFAs include the sandwich method, competitive method, and adsorption–desorption method. Diverse ingenious materials such as gold nanoparticles and quantum dots have also been proposed for signal read-out. Different signal capture models, such as colorimetric and fluorescence methods, have been applied for sensitive and accurate detection of a single or multiple target microbe. Furthermore, in view of the unique properties of nucleic acid aptamers, several signal amplification methods can be further involved in the LFA to enhance the sensitivity for target detection. This review introduces the use of aptamers with different structural patterns and labeling types in recent years, as well as a variety of methods to detect microbes, especially for the rapid detection of pathogenic bacteria. Based on the excellent characteristics, the aptamer-based LFA presents more flexibility and selectivity for microbe detection with good applicability, specificity, and sensitivity and can better achieve low-cost, rapid detection. This study is expected to provide a reference for developing nucleic acid aptamer-based LFA technologies, especially for efficient and accurate diagnosis of corona virus disease 2019 (COVID-19), exploiting the novel application scope of LFA technology. © 2022 Chinese Academy of Sciences. All rights reserved.

Electrochemical biosensing based on graphene for detection of the SARS-Co V-2 Nucleocapsid Protein

Monitoring and controlling infection is required in order to prevent the progression of the coronavirus severe acute respiratory syndrome 2(SARS-Co- V-2). To accomplish this goal, the development and implementation of sensitive, quick and accurate diagnostic methods are essential. Electrochemical sensors have exposed large application possibilities in biological detection due to the advantages of high sensitivity, short time-consuming and specificity. Here, we report the improvement of a sensitive electrochemical sensor capable of detecting the presence of the SARS-CoV-2 virus using graphene-modified interdigitated working electrodes functionalized with antibodies targeting the SARS-CoV-2 nucleocapsid protein (N protein). © 2022 IEEE.

Covid-19 Knowledge Deconstruction and Retrieval: Solutions of Intelligent Bibliometrics

Covid-19 is an unprecedented challenge that disruptively reshapes societies and scientific research communities. Facing the knowledge flood brought by the overwhelming volume of research efforts, there still lacks a platform to link those to previous knowledge foundations and efficiently visualize and understand them. Aiming to fill this gap, we propose a research framework in this paper to assist scientists in identifying, retrieving, and visualizing the emerging Covid-19 knowledge. The proposed framework incorporates principal topic decomposition (PCD), text analytics-based knowledge model (KM), and the hierarchical topic tree (HTT) method to profile the research landscape, retrieve knowledge of specific interest, and visualize the knowledge structures. Initially, our topic analysis of 127, 971 research papers published during 2020-2021 identified 35 research hotspots. Furthermore, we built up a knowledge model on the topic of vaccination and retrieved 92, 286 research papers from the entire PubMed database as the knowledge foundation of this topic. Lastly, the HTT results of the retrieved papers highlighted multiple relevant disciplines, from whose branches we identified four future research directions: Monoclonal antibody treatments, vaccination in diabetic patients, vaccination effectiveness in SARS-CoV-2 antigenic drift, and vaccination-related allergic sensitization. © Copyright 2022 for this paper by its authors.