Impedance Biosensors for the Rapid Detection of Viral and Bacterial Pathogens Using Avian Influenza Virus Subtypes H5N1 and H7N2 and Escherichia coli O157:H7 as Model Targets

This research investigated impedance biosensors for the rapid detection of viral and bacterial pathogens using avian influenza virus (AIV) subtypes H5N1 and H7N2 and Escherichia coli O157:H7 as the model targets, which were chosen due to their impact on the agricultural and food industries. For the detection of AIV H7N2, a single stranded DNA aptamer was selected using systematic evolution of ligands by exponential enrichment (SELEX). The selected aptamer and a previously selected aptamer against AIV H5N1 were used in a microfluidics chip with an embedded interdigitated array microelectrode to fabricate an impedance biosensor for specific detection of AIV H7N2 and H5N1. The developed label-free biosensor was capable of detecting AIV H7N2 and H5N1 at a concentration down to 27×10-4 hemagglutinination units (HAU) in 30 min without sample pre-treatment, comparable to previously designed biosensors though with the advantage of DNA aptamers. Two impedance biosensors based on the use of screen-printed interdigitated electrodes were developed for the detection of E. coli O157:H7. The first was a label-free biosensor based on magnetic separation and concentration of target bacteria using antibody-labelled magnetic nanobeads and Faradic impedance measurement. It was capable of detecting 1400 cells or more of E. coli O157:H7 in a total detection time of 1 h. COMSOL Multiphysics software was used to analyze the biosensor using a simplified model and determine the role of the magnetic nanobeads in the impedance measurement. The second biosensor for detection of E. coli O157:H7 was based on aptamer-labeled magnetic nanobeads and glucose oxidase/Concanavalin A-coated gold nanoparticle labels. This biosensor was capable of detecting 8 cells or more of E. coli O157:H7 in 1.5 h. The lower detection limit of the developed impedance biosensor was comparable to the most sensitive biosensors published for the detection of E. coli O157:H7 and was also more rapid and more practical for in-field tests. Multiple impedance biosensor designs were developed in this research. The developed biosensor for AIV could conceivably be adapted for detection of other AIV subtypes and the developed E. coli O157:H7 biosensors could easily be adapted to detect different bacterial pathogens

Biosensors for Rapid Detection of Avian Influenza

The scope of this chapter was to review the advancements made in the area of biosensors for rapid detection of avian influenza viruses (AIVs). It is intended to provide general background about biosensor technology and to discuss important aspects for developing biosensors, such as selection of the suitable biological recognition elements (anti-AIV bioreceptors) as well as their immobilization strategies. A major concern of this chapter is also to critically review the biosensors’ working principles and their applications in AIV detection. A table containing the types of biosensor, bioreceptors, target AIVs, methods, etc. is given in this chapter. A number of papers for the different types of biosensors give hints on the current trends in the field of biosensor research for its application on AIV detection. By discussing recent research and future trends based on many excellent publications and reviews, it is hoped to give the readers a comprehensive view on this fast-growing field

A Portable Impedance Biosensing System based on a Laptop with LabVIEW for Rapid Detection of Avian Influenza Virus

Avian Influenza Virus (AIV) H5N1 is a highly pathogenic virus found not only in birds but also in human. Rapid and sensitive detection method is needed to help prevent the spread of AIV H5N1. In this study, a portable impedance biosensing system based on a laptop with LabVIEW software was developed for detection of AIV H5N1. First, a virtual instrument was programmed with LabVIEW software to form a platform for impedance measurement, data processing and control. The audio card of a laptop was used as a function generator while a data acquisition card was used with the signal channels for data communication. A gold interdigitated microelectrode was coated with specific aptamers to bind H5N1 virus and used in a microflow cell to obtain changes in impedance with desired accuracy and sensitivity. A sampling delivery unit consisted of a pump and three valves and was controlled by the virtual instrument to provide automated operation with adjustable flow rate. Results of the impedance measured with this biosensing system were compared with a commercial IM 6 impedance analyzer, and the error was less than 5%. The experiments on AIV H5N1 virus showed a linear relationship between the impedance change and the concentration of AIV H5N1 in a detection range from 2 to 16HAU.The specificity for detection of AIV H5N1 was confirmed with three non-target AIV subtypes, H1N1, H5N2, and H5N3.The biosensing system is portable and automated and has great potential to serve as a diagnostic and epidemiological tool for in-field rapid detection of AIV and other pathogens

Identification and Biosensing Application of Molecular Recognition Elements

Molecular recognition elements (MREs) are biomolecules such as single-stranded DNA (ssDNA), RNA, small peptides and antibody fragments that can bind to user defined targets with high affinities and specificities. This binding property allows MREs to have a wide range of applications, including therapeutic, diagnostic, and biosensor applications. The identification of MREs can be achieved by using the process called Systematic Evolution of Ligands by Exponential Enrichment (SELEX). This process begins with a large library of 109 to 1015 different random molecules, molecules that bind to the user defined target or positive target are enriched in the process. Subsequently, this process can be modified and tailored to direct the enriched library away from binding to related targets or negative targets, and thus increasing the specificity. Single-stranded DNA (ssDNA) MREs are particularly favorable for biosening applications due to their relative stability, reusability and low cost in production. This work investigated the identification and application of ssDNA MREs to detect different bacterial toxins and pesticide.;In Chapter 1, it begins by reviewing recent discovery and advancement in the SELEX technique for the identification and biosensing application of ssDNA MREs specific for bacteria, viruses, their related biomolecules, and selected environmental toxins. It is then followed by a brief discussion on major biosensing principles based upon ssDNA MREs. In Chapter 2, the pilot project of this work, ssDNA MRE specific for Pseudomonas aeruginosa exotoxin A was identified. In this chapter, a novel variation of SELEX called Decoy-SELEX, previously developed by our laboratory is described in greater detail. Additionally, the development of a ssDNA MRE modified enzyme-linked immunosorbent assay (ELISA) for the exotoxin A detection is also discussed. In Chapter 3, similar methodology was applied to identify a ssDNA MRE specific for the second target, Clostridium difficile toxin B. Subsequently, similar ssDNA MRE modified ELISA was developed for target detection in clinically relevant samples. In Chapter 4, ssDNA MRE specific for alpha toxin of Staphylococcus aureus was identified, and it was applied for sensitive detection of the target in clinically relevant samples. In Chapter 5, the overall conclusion and potential future studies as a result from this work is discussed. Lastly, in Appendix, the project of identifying and potential future application of ssDNA MREs specific for a pesticide, Fipronil is described.;Overall, this work has shown the proof-of-principle of using ssDNA MREs in biosensing application for target detections in clinically relevant samples. The work will be useful in the development of potential point-of-care diagnostic tools for rapid diagnosis of bacterial infections

Biosensors: A Fast-Growing Technology for Pathogen Detection in Agriculture and Food Sector

Agriculture and food have a greater role to play in order to achieve sustainable development goals. Therefore, there is a need to put an end to the effect of pathogens on food quality and safety. Pathogens have been recognized as one of the major factors causing a reduction in profitable food production. The conventional methods of detecting pathogens are time-consuming and expensive for the farmers in rural areas. In view of this, this chapter reviews the biosensors that have been developed for the detection of biological hazards in food and agricultural sectors. This chapter also lays emphasis on the impact of nanotechnology on building a fast, reliable, more sensitive, accessible, user-friendly and easily adaptable technology for illiterate farmers in the rural communities. On the whole, we have addressed the past and most recent biosensors that could ensure the quick delivery of vision 2030 which aims to end hunger and poverty

Aptamer-based SPR Biosensor for Detection of Avian Influenza Virus

Rapid and specific detection of avian influenza (AI) virus is urgently needed with the concerns over the outbreaks of highly pathogenic H5N1 avian influenza in animal and human infection. Aptamers are artificial oligonucleic acid that can specifically bind to target molecules. They show comparable affinity for target virus and better thermal stability than monoclonal antibodies. Those advantages make aptamers promising candidates in diagnostic and detection applications. The goal of this research was to use DNA&ndashaptamer as the specific recognition element in a portable surface plasmon resonance (SPR) biosensor for detection of AI H5N1 virus in poultry. A SPR biosensor was fabricated using the selected aptamers based on streptavidin&ndashbiotin method. Streptavidin was directly adsorbed onto the surface of a gold waveguide in the SPR biosensor. Then, biotinylated aptamers were immobilized on the sensor surface via streptavidin&ndashbiotin binding. The immobilized aptamers captured AI H5N1 virus in a sample solution, causing an increase in refraction index (RI). Performances of the aptamer&ndashbased SPR biosensor were studied in streptavidin modification, aptamer immobilization and virus detection. The optimal concentrations of streptavidin and aptamers were determined to improve the sensitivity of the biosensor. The response of the aptamer&ndashvirus interaction was shown to be virus titer&ndashdependent, and a linear range for the titers of AI H5N1 was found between 0.128 and 1.28 HA unit. The aptamer&ndashbased SPR biosensor could detect the H5N1 virus at a titer greater than 0.128 HA unit within 1.5 h. No significant interference was observed from non&ndashtarget subtypes such as AI H7N2, H9N2, H2N2, H1N1 and H5N2. The aptamer&ndashbased SPR biosensor was further evaluated for detection of AI virus in poultry swab samples. All of the AI viruses used in this study were killed ones to ensure biological safety

Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched

CRISPR-Cas- and Aptamer-based Systems for Diagnosing Pathogens: A Review

Pathogenic infections cause severe clinical illnesses in humans and animals. Increased encounters between humans and animals and constant environmental changes exacerbate the transmission of zoonotic infectious diseases. Recently, the World Health Organization has declared some zoonotic epidemics as public health emergencies of international concern. Hence, rapid and accurate detection of the causative pathogen is particularly essential in combating emerging and re-emerging infectious diseases. Traditional pathogen detection tools are time-consuming, costly, and require skilled personnel, which greatly hinder the development of rapid diagnostic tests, particularly in resource-constrained regions. Clustered regularly interspaced short palindromic repeats (CRISPR-)-Cas- and aptamer-based platforms have replaced traditional pathogen detection methods. Herein we review two novel next-generation core pathogen detection platforms that are utilized for clinical and foodborne pathogenic microorganisms: CRISPR-Cas-based systems, including dCas9, Cas12a/b, Cas13, and Cas14; and aptamer-based biosensor detection tools. We highlight CRISPR-Cas- and aptamer-based techniques and compare the strengths and weaknesses. CRISPR-Cas-based tools require cumbersome procedures, such as nucleic acid amplification and extraction, while aptamer-based tools require improved sensitivity. We review the combination of CRISPR-Cas- and aptamer-based techniques as a promising approach to overcome these deficiencies. Finally, we discuss Cas14-based tools as functionally stronger platforms for the detection of non-nucleic acid targets