Next Article in Journal
Single Camera-Based Dual-Channel Near-Infrared Fluorescence Imaging system
Next Article in Special Issue
The Effect of Glycerol-Based Suspensions on the Characteristics of Resonators Excited by a Longitudinal Electric Field
Previous Article in Journal
Reliability of Repeated Nordic Hamstring Strength in Rugby Players Using a Load Cell Device
Previous Article in Special Issue
A Denoising Method for Mining Cable PD Signal Based on Genetic Algorithm Optimization of VMD and Wavelet Threshold
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Immunosensors—The Future of Pathogen Real-Time Detection

1
Biohazard Prevention Centre, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland
2
Military Institute of Armored and Automotive Technology, Okuniewska 1, 05-070 Sulejowek, Poland
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(24), 9757; https://doi.org/10.3390/s22249757
Submission received: 4 November 2022 / Revised: 7 December 2022 / Accepted: 10 December 2022 / Published: 13 December 2022
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors III)

Abstract

:
Pathogens and their toxins can cause various diseases of different severity. Some of them may be fatal, and therefore early diagnosis and suitable treatment is essential. There are numerous available methods used for their rapid screening. Conventional laboratory-based techniques such as culturing, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) are dominant. However, culturing still remains the “gold standard” for their identification. These methods have many advantages, including high sensitivity and selectivity, but also numerous limitations, such as long experiment-time, costly instrumentation, and the need for well-qualified personnel to operate the equipment. All these existing limitations are the reasons for the continuous search for a new solutions in the field of bacteria identification. For years, research has been focusing on the use of immunosensors in various types of toxin- and pathogen-detection. Compared to the conventional methods, immunosensors do not require well-trained personnel. What is more, immunosensors are quick, highly selective and sensitive, and possess the potential to significantly improve the pathogen and toxin diagnostic-processes. There is a very important potential use for them in various transport systems, where the risk of contamination by bioagents is very high. In this paper, the advances in the field of immunosensor usage in pathogenic microorganism- and toxin-detection, are described.

1. Introduction

Pathogens and their toxins possess the ability to adversely affect humans and animals with a range of relatively mild reactions up to severe course of disease, and in some cases can cause death [1,2]. Pathogens include organisms such as bacteria, viruses, fungi and parasites [3,4], and they can be found in numerous environments including water, soil and air [5,6,7]. Pathogens can be transmitted from their natural reservoir to a susceptible host in different ways. The mode of transmission can be characterized on the basis of pathogens spreading in direct and indirect ways. In direct transmission, pathogens are transferred by direct contact or by droplet dispersal. Indirect transmission occurs when pathogens can be transmitted by suspended air particles, inanimate objects (water, food, blood, bedding, surgical equipment, toys, environment), or animate intermediaries (mosquitoes, fleas, ticks) [8,9]. In addition, pathogens possess the ability to rapidly evolve as well as to adapt and grow under different conditions such as low or high temperatures, basic or acidic pH, a wide range of salinities and various pressures [10]. Biological toxins consist of harmful substance produced by various organisms such as: bacteria, fungi, insects, vertebrate and invertebrate animals, and plants, mainly for defensive purposes [2,11]. These molecules can also be present in various environments and induce detrimental effects in other organisms, which can contract them by injection, inhalation or absorption [12].
Rapid detection of pathogens and toxins is of the greatest importance primarily for health and safety reasons and reducing the risk of pandemic contamination. The food industry, water and environmental quality control and clinical diagnostics are the main areas where prompt biological-agent detection is crucial [2,13]. The existing methods used to detect pathogens and toxins rely on conventional techniques such as plate culturing, enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) [14,15]. Plate culturing is one of the oldest method of pathogen identification; however, it still remains the “gold standard” for bacteria detection, due to its high sensitivity and selectivity. In spite of this, the culturing technique requires selective plating, several days for enrichment, identification, and confirmation and numerous microbiological procedures that are time-consuming and monotonous [16,17]. Immunological assay ELISA is also used to detect pathogens, and is one of the most popular immunoassay methods. This technique can provide results slightly faster compared to plate culturing, but the high number of false positives and the experimental complexity limit its use [18,19,20]. The molecular-based detection method—PCR, for example—is a very popular technique for pathogens’ detection [21]. Specific pathogens based on their nucleic acid sequence are targeted when PCR is used for detection. PCR can detect a single copy of the target DNA sequence, and thus can be used to detect, for example, a single bacterium in a food sample [22,23]. This method can be used for pathogen detection with high specificity and sensitivity, but requires costly instrumentations, several steps of procedure and well-qualified personnel to operate the whole experiment and to interpret the obtained results [24]. In spite of disadvantages such as the complexity of use or the time required for the analysis, it is still used successfully as an effective detection tool. The above techniques are often combined to obtain more reliable results [13,25,26].
Despite the effectiveness of conventional methods, there is a need for new technology that is simple, rapid, specific, sensitive and reliable. Moreover, it should be appropriate for in situ real-time monitoring at low cost. In recent years, there has been increased research activity in the field of biosensors development for the detection of pathogenic microorganisms and toxins [27,28]. A biosensor is an analytical device, which integrates a biologically derived molecular-recognition molecule into a suitable physicochemical-transducing mechanism and converts a biological response into an electrical signal [29]. Biosensors consist of two principal elements: a bioreceptor or biorecognition component that recognizes the target analyte, and a transducer that converts the recognition event into a measurable electrical signal (Figure 1). The bioreceptor can be the tissue, cell, enzyme, antibody, nucleic acid, microorganism, organelle and others. Common transducing elements are: electrochemical, optical, piezoelectric, thermometric, magnetic, micromechanical, or combinations of one or more of those techniques [30].
Compared to conventional techniques, biosensors do not require highly qualified personnel. Furthermore, if a biosensor is highly selective and sensitive, it can provide results faster than standard methods, making it ideal for practical and field applications [31,32]. This paper aims to give an overview of pathogen and biological-toxins detection using immunosensors. It describes different electrochemical, optical, and piezoelectric platforms for the detection of different pathogens and biological toxins.

2. Methods

A literature search was carried out using PubMed and Medline databases. A total number of 137 articles were analyzed, including 80 original research papers and 57 reviews (meta-analyses, systematic reviews, literature reviews). In this paper, we have included articles mainly from the last 10 years. All scrutinized articles focused on biosensors proposed for the detection of pathogens and biological toxins, with particular emphasis on their clinical capabilities and use in point-of-care diagnostics. Moreover, all articles were published in the English language. Search terms included “biosensors”, “immunosensors”, “electrochemical biosensors”, “piezoelectric immunosensors”, “optical immunosensors”, “conductometric platform”, “impedimetric platform”, “potentiometric platform”, “amperometric platform”, “biosensors bacterial diagnostic”, “biosensors viral diagnostic”, “biosensor point-of-care diagnostic”. Therefore, we excluded papers not published in English. Independently, the three authors searched the databases for articles on types and descriptions of biosensors, and three for the clinical application of biosensors.

3. Immunosensors

Immunosensors are a type of affinity solid-state based biosensors, in which the target analyte, antigen (Ag), is detected by the formation of a stable complex between Ag and the antibody (Ab) as a capturing agent. This immunological reaction results in the generation of a measurable signal given by the transducer [33]. The action of immunosensors may be similar to immunoassays, but there is a subtle difference between them. The immunoassay test is a solid phase system in which the Ag-Ab complex takes place, but the detection is carried out elsewhere. In immunosensors, interaction between Ab and Ag, and the recognition process of the Ag occur within the same platform [34,35]. Based on their transduction mode, immunosensors can be classified into three main types, including electrochemical (amperometric, potentiometric, impedimetric, and conductometric), and optical and piezoelectric devices [36]. Depending on the transducer type and the signal-processing modes, immunosensors are divided into label-free and labeled sensors [37]. Label-free immunosensors measure the physical or chemical changes resulting from the Ag-Ab immune-complex formation without labeling [38]. Label-free detection reduces the preparation time, sample complexity and analysis cost, and enables detection of target-probe binding in real-time, which is generally not possible with label-based systems [39]. However, a problem with the use of label-free immunosensors can be the non-specific adsorption on their response. In general, in the absence of Ag-Ab interaction, no signal should be observed; nevertheless, a slight signal can always be obtained because of the non-specific Ag or different proteins binding to the substrate’s surface [40]. This phenomenon occurs due to the presence of other proteins in the sample that can adsorb to the Abs or support surface, leading to an increase in the background signal. The consequence of the non-specific adsorption is a decrease in sensitivity. Therefore, it is necessary to use a suitable blocking agent. A number of compounds are used as blocking agents, such as: casein, bovine serum albumin (BSA), and other milk proteins, surfactants (polyethylene glycol, Tween 20), and thionic compounds for gold surfaces [33,41]. Labeled immunosensors use signal-generating labels, such as enzymes (catalase, glucose oxidase), fluorescent dyes, and metal ions, and also nanomaterials, such as gold nanoparticles (AuNPs), carbon dots (C-dots) and quantum dots (QDs) [37,42]. In this type of immunosensor labels can be attached to the Ab or Ag, resulting in electron-transfer and assuming that happens, the number of labels detected during measurement correlates with the number of target analytes [33]. Compared to label-free, labeled immunosensors possess a lower effect of non-specific signal adsorption and higher versatility and sensitivity, due to the analytical characteristics of the applied label. Disadvantages include the inability for real-time monitoring of the Ag-Ab reaction and high operation and development costs [37,40]. Labeled immunosensors can be further divided into two other types of assays: the competitive and the sandwich type, according to the analytes’ molecular size. Competitive-type assays are applied for the small-molecule compounds (e.g., pesticides) with a small molecular weight and only one epitope. The analytes in the samples are measured based on their ability to compete with the labeled Ag in the immunosensors. The signal obtained from the labeled analyte is inversely proportional to the sample amount of the analyte. Thus, the responses decrease as the concentration of the analyte increases [37,43]. Sandwich-type assays are preferred for macromolecular compounds with high molecular weight (e.g., proteins) and more than one epitopes. The detected signal-responses are directly proportional to the number of analytes in the analyzed samples [33,37].

3.1. Electrochemical Immunosensors

Electrochemical transducers are the most commonly used methods in biosensors, and can be broadly classified into label-free and labeled sensor. The principle of this method is based on the selective identification of the Ag (analyte) by the capture Ab immobilized on the electrode surface. The label-free electrochemical immunoassay can determine the concentration of the analyte by direct measurement of the Ag-Ab’s specific recognition of the change in the electrochemical signal which is generated after binding. The sandwich-type electrochemical immunosensor additionally uses the detection Ab, which is often labeled as enzymes or fluorescent labels [44]. The signal is usually the result of a catalytic reaction of the enzyme molecule labeled as a signal tracer with the detection Ab. The electroactive product containing electric charges can be detected by the electrode [45]. The electrode can derive the signal which is generated on the electrode surface and convert it into an electrical signal, including voltage, current, and resistance, which can be measured and analyzed to obtain a qualitative or quantitative analysis of the analyte, e.g., toxin, pathogen, or disease biomarker (Figure 2) [43,46]. In general, electrochemical immunosensors can detect different analytes by measuring the change in potential, current, conductance, or impedance, caused by the immunoreaction. This type of immunosensor can be also classified as amperometric, potentiometric, impedimetric, and conductometric, depending on the type of signal [47].

3.1.1. Amperometric Platform

This method is based on the measurement of a current flow that relates to the concentration of a measured analyte such as a pathogen. The amperometric platform applies a constant potential at the working electrode related to the reference electrode, where the potential is obtained from the electrochemical oxidation or electroreduction of an electroactive species [48,49]. This system has several advantages, including low costs and sensitivity. It can be used in conjunction with mediators such as iodine or ferrocenedicarboxylic acid (FEDC), to improve their selectivity. Moreover, there is great potential for miniaturization of this system, which leads to smaller sample volume [50]. Over the years, amperometric immunosensors have been used to detect various pathogens. This system was used for the detection of Escherichia coli O157:H7 in food specimens. The method relies on first, the long-chain, amine-terminated alkanethiol 11-amino-1-undecanethiol hydrochloride (AUT) self-assembling on a gold electrode surface and providing an ordered, oriented, stable and compact monolayer for the immobilization of massive AuNPs. Next, chitosan-multiwalled carbon nanotubes–SiO2/thionine (CHIT–MWNTs–SiO2@THI) composite is synthesized and attached to an electrode surface. According to the results, E. coli O157:H7 was detected in milk and water samples with a limit of detection (LOD) at 2.5 × 102 colony-forming unit (CFU)/mL [51]. The amperometric immunosensor system was used for the detection of Mycobacterium tuberculosis in sputum samples. In this study, an amperometric biosensor with microtip immunoassay was used. The system is based on a focused amperometric measurement produced by a high electric-field and concave-meniscus profile near the microtip area. Detection antibodies were specifically captured on the microtip area, and the electrical current was increased upon the capture of M. tuberculosis. The LOD was 1 × 102 CFU/mL [52]. In a different study, a disposable enzyme-labeled amperometric immunosensor for Listeria monocytogenes detection was developed. The immunosensor was developed by immobilizing the horseradish peroxidase (HRP)-labeled antibody against L. monocytogenes onto the surface of the novel multiwalled-carbon-nanotube (MWCNT)-fiber electrode. This immunosensor exhibited acceptable reproducibility, specificity, and stability, and the detection limit was 1.07 × 102 CFU/mL [53]. A label-free amperometric immunosensor for hepatitis-B-surface-Ag determination was also developed. The immunosensor was based on the immobilization of Ab molecules on a biocompatible redox-active poly(allylamine)-branched ferrocene (PAA-Fc)/AuNPs glassy-carbon electrode. The PAA-Fc composite retains its electrochemical activity, avoids the leakage of Fc, and enhances the conductivity of the composite. The AuNPs adsorption onto the PAA-Fc matrix provides sites for Ag immobilization and a favorable microenvironment for maintaining its activity. The method is efficient, cost-effective, potentially attractive for clinical immunoassays, and the LOD was 40 pg/mL [54]. The examples of the use of an amperometric immunosensor for pathogen detection are summarized in Table 1.

3.1.2. Potentiometric Platform

Measuring the change of potential due to the formation of the immunocomplex between Ab and Ag is the principle of potentiometric immunosensors. In this method, the conversion of the biorecognition process into a change in potential signal is detected by a reference electrode [33]. The label-free potentiometric immunosensor for Salmonella typhimurium detection was developed. The immunosensor is based on the surface-blocking principle and a zero-current passive-ion-flux developed on a paper-based platform. A paper-strip ion-selective electrode with a carboxylated-polyviny- chloride (PVC)-membrane was integrated with a filter-paper pad, which acted as reservoir for the internal solution. The limit of detection was established on 5 cells/mL [58]. Silva et al. [59] applied a label-free potentiometric immunosensor toward S. typhimurium. The signal-output amplification was applied to a gold nanoparticle polymer-inclusion-membrane (AuNPs-PIM) that was used as a sensing platform and also for antibody immobilization. Moreover, a marker ion was used to detect the Ab-Ag binding event at the electrode surface. A detection limit of 6 cells/mL was attained. In another study, a silicon-chip-based light-addressable potentiometric sensor (LAPS) assay was utilized to detect S. typhimurium. Biotinylated and fluorescein-labelled anti-Salmonella Abs were selected as biorecognition elements. The sensitivity of this assay was approx. 1.19 × 102 CFU/mL [60]. A different potentiometric immunoassay for the detection of enterovirus 71 (EV71) was also developed, using a silver (Ag+) ion-selective electrode (ISE). First, carboxylated dendrimer-doped AgCl nanospheres were synthesized and used to label mouse anti-EV71-detection pAbs using the carbodiimide coupling procedure. The immunoreaction was performed on an anti-EV71-capture mAb-coated microplate, using a biofunctional AgCl nanosphere as the detection Ab. This assay was carried out with a sandwich-type immunoassay format. The potential was monitored by using a digital ion-analyzer with a two-electrode system consisting of Na-ISE as the reference electrode and Ag-ISE as the working electrode. The LOD was established at 0.058 ng/mL [61]. A summary of the use of the potentiometric method in the detection of various pathogens is presented in Table 2.

3.1.3. Impedimetric Platform

In this type of immunosensor, the impedance of the sensor is measured, which is affected by the biological reaction [63]. Electrochemical impedance spectroscopy (EIS) is an effective technique for the investigation of the formation of complexes among biomolecules on the surface of an electrode by probing the electrode/electrolyte interfacial properties. EIS measures a small sinusoidal-AC-voltage perturbation signal and measures the resulting AC current. These measurements are often fitted with the Randles equivalent-electric-circuit and impedimetric signal, which relies on the change in one of these equivalent-electric-circuit parameters upon analyte binding [64]. A microfluidic flow-cell with an embedded-gold interdigitated-array microelectrode (IDAM) was developed and integrated with magnetic nanoparticle-antibody conjugates (MNAC) into an impedance immunosensor for the purpose of E. coli O157:H7 detection. This system is able to detect 1.6 × 102 in pure culture and 1.2 × 103 cells of E. coli O157:H7 in ground-beef samples in 35 min [65]. In another study, a polydimethylsiloxane (PDMS) microfluidic-impedance immunosensor integrated with a specific Ab-immobilized alumina-nanoporous membrane was developed for detection of Staphylococcus aureus. The Abs were covalently immobilized onto nanoporous-alumina membranes via self-assembled (3-glycidoxypropyl) trimethoxysilane (GPMS) silane. The nanoporous alumina membrane is used in impedimetric immunosensing because of the increase in the electron-transfer through the electrode-solution interface caused by its high pore-density, biocompatibility and extension of surface area. This immunosensor provides e bacteria detection within 2 h with a high sensitivity of 1 × 102 CFU/mL [66]. A nonstructural-Ab (NS1)-based impedimetric immunosensor, coupled with a bovine-serum-albumin (BSA)-modified screen-printed carbon electrode (SPCE) as the transducing substrate for the early diagnosis of dengue virus, were also developed. In this method, first, the anti-NS1 monoclonal Ab (mAb) is immobilized on the electro-grafted BSA surface of the working electrode. Then, the change in electron-transfer resistance with NS1 interaction is monitored, using EIS. This immunosensor successfully detected the dengue-virus protein with an LOD of 0.3 ng/mL [67]. An electrochemical-impedance immunosensor was also used to directly detect toxins, e.g., ricin. The nanoporous-aluminum substrate was hydrophobically modified via the self-assembled monolayer of 3-aminopropyltriethoxysilane (APTES). An immunosensor for the ricin detection was fabricated using the covalent cross-linking of Ab with self-assembled APTES. It detected the presence of ricin in milk, vegetable soup, and tomato juice containing 500 ng/mL of toxin in 20 min [68]. EIS can also be used for the detection of trace concentrations of Staphylococcal enterotoxin B (SEB). An anti-SEB Ab is attached to the nanoporous-aluminum surface using the APTES/glutaraldehyde coupling system. This immobilization technique allowed the fabrication of a highly stable and reproducible sensing device. Using this system, it is possible to determine the presence of SEB in concentrations as low as 10 pg/mL, in 15 min [69]. The application of the impedimetric method for different pathogens detection is shown in Table 3.

3.1.4. Conductometric Platform

The conductometric immunosensor is based on a relationship between the biorecognition and conductance events [70]. When the reaction between the biorecognition component and Ag occurs, the conductivity of the current flow or solution is changed, due to the change in the concentration of the ionic species [71]. The biological signal is converted to an electrical signal through a conductive polymer such as polyaniline, polyacetylene or polypyrrole [72]. Polyaniline is the most extensively used conductive polymer, due to its strong biomolecular interactions, good conductivity and environmental stability [73]. A conductometric immunoassay for hepatitis B surface Ag (HBsAg) was developed. The assay relied on the bio-electrocatalytic reaction on the microcomb-type electrode using double-codified nanogold particles as labels. The microcomb-type electrode was produced on a transducer covered with an ordered anti-HBs/protein A/nanogold-architecture. The double-codified nanogold particles were prepared by using nanogold-labeled anti-HBs Abs conjugated with horseradish peroxidase (HRP). The formation of the immunocomplex changed the direct electrical communication between the electrode and carried HRP, and thus local conductivity variations could be determined based on the bio-electrocatalytic reaction of the carried HRP. The described immunosensor exhibited a low detection limit of 0.01 ng/mL HBsAg [74]. An acetylcholinesterase-based conductometric biosensor was developed for the detection of aflatoxin B1 (AFB1). Acetylcholinesterase immobilized onto the surface of the conductometric transducer was used as the bio-selective element. The LOD of AFB1 was established at 0.05 μg/mL [75]. A conductimetric immunosensor incorporating a polyclonal Ab (pAb) sandwich-assay was developed, in which the Ab-detection labelled with polyaniline was developed for detecting E. coli O157:H7 and Salmonella spp. This immunosensor could detect 79 CFU/mL of E. coli O157:H7 and 83 CFU/mL of Salmonella spp. within 10 min [76]. A conductometric immunoassay based on magnetite nanoparticles for E. coli detection was also reported. The nanoparticles were directly immobilized on the conductometric electrode, using glutaraldehyde coupling. Biotinylated anti-E. coli Abs were immobilized on streptavidin-modified magnetite nanoparticles by biotin–streptavidin interaction. The incorporation of nanoparticles facilitated the increase in conductivity, allowing the detection of 0.5 CFU/mL of bacteria [77]. Table 4 summarizes the application of this method in pathogen identification.

3.2. Optical Immunosensors

The optical sensor system contains a light source, several optical components for generating a light beam with specific characteristics and directing this light to the modulating agent, a modified sensing head, and a photodetector [43]. Optical immunosensors can detect changes in optical properties in the evanescent field of an optical surface wave, in order to quantify Ag-Ab interactions. The evanescent field is generated when reflected and incident beams interfere with each other (Figure 3) [78].
Fiber-optic immunosensors are based on the measurement of fluorescent light excited by an evanescent wave generated by a laser to quantitatively detect biomolecules immobilized on the fiber surface [79]. This type of assay has been used to detect the Clostridium botulinum toxin. Abs specific for botulinum toxin were immobilized on the fiber surface. When the toxin bound to the surface, a second Ab labeled with tetramethylrhodamine-5-isothiocyanate (TRITC) was used for signal generation. Using the fiber evanescent wave, the binding events along the core of the tapered fiber were transduced as an increase in fluorescence intensity. The botulinum toxin was detected within a minute, at concentrations as low as 5 ng/mL [80]. The evanescent-wave fiber-optic biosensor for ricin detection was developed. A sandwich-immunoassay scheme was used to detect the ricin toxin. The avidin-coated fibers were incubated with biotinylated anti-ricin IgG to immobilize the Ab, using an avidin–biotin bridge. The LOD of ricin in river-water samples was established at 1 ng/mL. The entire assay was performed on previously prepared fibers within 20 min [81]. Another fiber-optic immunosensor to detect low levels of Listeria monocytogenes cells was developed. In this method, first, pAb is immobilized on polystyrene fiber waveguides through the biotin-streptavidin reaction, to capture bacteria cells on the fiber. Next, cyanine 5 (Cy5)-labeled murine mAb is used to generate a specific fluorescent signal. The sensitivity range is approx. 4.3 × 103 CFU/mL for a pure culture of L. monocytogenes [82]. Morlay et al. [83] developed a label-free system based on surface plasmon resonance (SPR) imaging, which is an optical detection technique used to monitor and analyze biomolecular interactions in real time, coupled with an immunosensor specific to L. monocytogenes detection. A biochip covered with a gold layer was functionalized with different pAbs. During the analysis, the SPR signal was monitored in real time during the injection of different bacterial concentrations. The Ab successfully bound bacterial cells in lettuce samples inoculated with L. monocytogenes strains. This approach allowed the detection of a very small number of bacteria in foodstuff (from 17 to 25 CFU/25 g of lettuce). In a different study [84], an SPR sensor platform for Campylobacter jejuni detection was developed. SPR sensor chips were functionalized with pAbs against C. jejuni using covalent attachment, and then gold chips were applied for the direct detection of bacteria. Three different immunoassay-formats (direct, sandwich and sandwich-with-Ab-functionalized-AuNPs) were developed for the detection of C. jejuni on an SPR device. According to the results, the best immunoassay was the sandwich one, and the poorest was the direct immunoassay. The LOD obtained for the detection of bacterial cells using the sandwich immunoassay was 4 × 104 CFU/mL. A sandwich SPR-immunosensor for the detection of SEB was also developed. The anti-SEB Abs were bound covalently onto the gold-chip surface, via attachment to carboxymethyl-dextran on the chip surface. The SPR-biosensor assay detected SEB at 10 ng/mL within 8 min [85]. The above-mentioned applications of the method are summarized in the Table 5.

3.3. Piezoelectric Immunosensors

Piezoelectric immunosensors are based on materials such as quartz crystals with Ab or Ag immobilized on their surface, and can be employed by the application of an external alternating-electric-field or by pH changing (Figure 4). The oscillation frequency is proportional to the change in quartz-crystal mass. The reaction between Ab and Ag (one immobilized on the surface and the other free in gas phase or solution), can be followed in real time. Factors such as effective viscosity, conductivity, electrode morphology, dielectric constant, density and temperature of the liquid, can also influence the frequency responses [43].
Oztuna et al. [86] presented an aminated-poly(vinyl chloride) (PVC-NH2) coated by the piezoelectric crystal immunosensor for the simultaneous, rapid detection of Bacillus anthracis spores. PVC-NH2 was used as an adhesive layer for mAb immobilization on gold quartz crystal [86]. Experiments conducted on primates showed that the estimated infective dose for Bacillus anthracis is 8000–50,000 spores [87]. The prepared immunosensor was tested in the range of infective doses mentioned above, and the detection limit was estimated as 2187 spores [86]. The immunosensing device based on a piezoelectric-sensor detection of the Francisella tularensis was also developed. The immunosensor included mouse pAbs immobilized in a layer of protein A covalently linked to the gold electrode of the sensor. The immunosensor is able to detect F. tularensis with a detection limit of 1×105 CFU/mL in less than 5 min [88]. A piezoelectric immunosensor based on the amplification effect of the biotin-avidin system and the mass-multiplied effect of nano-gold particles was developed for abrin detection. The avidin is covalently attached to the biotin-labeled abrin pAbs, and is successfully immobilized to the gold electrode of the piezoelectric quartz crystal. The LOD was 0.05–5 mg/L [89]. A direct, label-free piezoelectric immunosensor was designed for the rapid detection of staphylococcal enterotoxin A (SEA), using quartz crystal microbalance with dissipation (QCM-D), as a transduction method. The sensing layer with the anti-SEA Ab was constructed using chemisorption of a self-assembled monolayer of cysteamine on the gold electrodes placed over the quartz crystal sensor, followed by the surface-amino-groups activation with the rigid homobifunctional cross-linker 1,4-phenylene diisothiocyanate (PDITC) and covalent linkage to the binding protein A. The LOD was 7 ng/mL for a total assay time of 25 min [90]. A direct and label-free immunoassay for SEB, based on a piezoelectric crystal immunosensor was fabricated. Three different immobilization methods were conducted: covalent immobilization based on the polyethyleneimine (PEI), covalent immobilization based on the self-assembled monolayer, and the protein A method. All of the immobilization methods used anti-SEB Abs undertaken on the gold-electrode PZ crystal on the gold electrode on one side of the piezoelectric crystal. The electrode coated with PEI showed the best results, and the self-assembled monolayer method provided the worst. The measurable range for SEB was 2.5–60 µg/mL, and the LOD was found to be 2.5 µg/mL [91]. Herein, we summarize the above-mentioned piezoelectric-immunosensor studies in the detection of different pathogens and toxins (Table 6).

4. Clinical Application of Biosensors and Real-Time Point-of-Care Test (POCT)

Biosensors have revolutionized diagnostics and medical care, and have also been used in many areas not directly related to medicine—environmental protection, food production, and even in bioterrorism countermeasures. The biosensor market is growing very dynamically. Analysts predict that its value may reach as much as USD 25 billion in 2021, and even USD 49.8 billion by 2030 [93]. Biosensors have been found in many applications in various fields, but they are probably the most widely used in medicine. In the previous decades, the highest percentage of biosensors was used in hospitals, in patient examination systems—from simple systems for monitoring blood glucose levels, to techniques for detecting tumor markers or the presence of viruses (e.g., HIV) in the body [94].
Point-of-care testing (POCT) as an innovative diagnostic technology began to be used on a large scale in medicine in the 1990s. POCT means not only diagnostics performed by the patient himself, but also quick diagnostics carried out by medical personnel in direct contact with the patient [95]. The use of POCT has become widespread in situations where large amounts of blood have to be drawn, to establish a diagnosis or to monitor treatment. This technique avoids iatrogenic anemia primarily in patients in intensive-care units, transplant patients, and especially in neonates and small children. The advantage of using POCT is the simplicity of the method. Medical personnel, such as nurses or paramedics, perceive the additional POCT obligation as not time-consuming [96]. Therefore, POCT in the form of biosensors is currently becoming one of the most important elements in the diagnosis of infectious diseases. An unquestionable advantage of using biosensors in the diagnosis of infectious diseases, apart from practicality, low invasiveness and low cost, is the possibility of their use a very short time after infection. In immunosensors, this time depends on the rate of production of specific antibodies, and therefore on the type of pathogen, whereas in nucleic-acid-based biosensors, the detection of a microorganism depends on the pathogen’s multiplication rate [97].

4.1. Bacterial Infection

Infectious diseases caused by pathogenic bacteria and those associated with high mortality require special attention from the public health system [98]. Staphylococcus aureus, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella typhimurium, Streptococcal bacteria, Mycobacterium tuberculosis, Bacillus cereus and Clostridium perfringens are among the most common pathogens causing bacterial infections in humans [99]. The perennial increased antibiotics-administration, with both the incorrect use of antibiotics in the disease treatments that do not require antibiotic therapy, and their addition to food, cause the emergence of resistant bacteria [100]. Conventional diagnostic tools, although reliable, often require a lot of time and generate high costs, and very often effective treatment depends on quick diagnosis. Thus, the use of biosensors will on the one hand speed up the process, and on the other hand will be much cheaper [1].
Staphylococcus aureus is one of the most dangerous human pathogens. It demonstrates complex pathogenesis mechanisms, has a rich arsenal of virulence factors, and is also characterized by high resistance to chemotherapeutic-agent strains [101]. Methicillin-resistant strains of S. aureus (MRSA), described in 1961 [102], and resistant to all beta-lactam antibiotics except for the latest cephalosporins specifically targeting MRSA, proved to be particularly dangerous. Conventional microbial cultures typically require 3–5 days, while nucleic-acid technologies are very expensive, so the use of biosensors seems to be an option. Hernandez et al. proposed the use of a graphene-based potentiometric aptasensor to detect live S. aureus cells. This biosensor is made of a transducer layer (graphene oxide—GO or reduced GO—) and with a DNA aptameter attached to it (a sensing layer). These biosensors are characterized by high selectivity and sensitivity (detection of a single CFU/mL); however, sensors based on a reduced GO show a lower noise-level. The basis of the biosensor operation is the change in the recorded potential related to the preference of the aptameter binding to bacteria [103]. A potentiometric biosensor mainly for detecting S. aureus food contamination was developed by Ahari et al. Selective patterns for the S. aureus exotoxin are used in this biosensor, which enables the identification of bacteria. Although the main application of this sensor is in food-quality monitoring, it also has potential for medical diagnostics [104]. A slightly different approach was presented by Suaifan et al., who constructed a biosensor based on S. aureus proteolytic activity, to detect infections in healthcare settings. The biosensor is built of a specific peptide substrate located between the magnetic nanospheres and the gold-coated paper support. The basis of its operation is a color change visible to the naked eye, resulting from the dissociation of magnetic nanospheres-peptides in the presence of S. aureus, while the use of specialized software enables quantitative measurement (detection limit: pure-broth culture: 7 CFU/mL; inoculated in food products: 40 CFU/mL; inoculated environmental samples: 100 CFU/mL) [105].
Escherichia coli is a gram-negative, relatively anaerobic bacterium belonging to the Enterobacteriaceae family, which colonizes the human intestines and other warm-blooded animals [106]. E. coli is an opportunistic bacterium, but some E. coli pathogenic strains, together with Salmonella enteritidis, Campylobacter jejuni, Shigella and Yersinia, are responsible for the majority of bacterial diarrhea. One of the most dangerous enterohemorrhagic E. coli serotypes is O157:H7, which produces Shiga-like toxin, and may lead to the potentially fatal hemolytic-uremic syndrome (HUS) [107]. Shigella, like E. coli, also belongs to the Enterobacteriaceae family, and causes gastrointestinal infections. Thus, the development of fast, sensitive, and relatively cheap techniques aiming to detect and monitor this bacteria, is an important task for modern medicine. Wan et al. developed an impedimetric biosensor for the sensitive detection of E. coli O157:H7. This sensor is based on the transfer of electrons through a self-assembling monolayer through gold nanoparticles, onto which antibodies against E. coli have been transplanted. The attachment of gold nanoparticles to the surface of the bacteria resulted in a significant reduction of the electron-transfer resistance between the probe in the solution and the gold surface of the substrate [108]. In turn, Xiao et al. constructed fiber-optic biosensors, on which DNA probes capable of hybridizing to fluorescently labeled complementary-DNA were immobilized to identify Shigella. Importantly, the authors suggest that the sensitivity of this technique was comparable to the PCR method [109]. An optical genosensor for the early detection of Shigella was developed by Elahi et al. This technique utilizes the Shigella Spa gene, which was hybridized with the AuNP-DNA probe. The principle of the method is the change in color from red to purple in the absence of a complementary target, due to the aggregation of AuNP probes, in an acidic environment, while in the presence of a specific sequence (one of the four Shigella strains) it remains red [110].
Tuberculosis (TB) is one of the leading causes of death from infectious diseases worldwide. According to a WHO report from 2021, TB remains a major public-health threat worldwide, and the suboptimal global response to TB worsened during the COVID-19 pandemic [111]. The commonly used skin-test has low specificity, due to possible false-positive results in healthy subjects vaccinated with Bacillus Calmette–Guerin (BCG); thus, rapid and sensitive diagnostic techniques appear to play a key role in successful TB treatment. Rapid tests detecting both active TB and TB drug-resistance enable the patient’s diagnosis, regardless of the laboratory infrastructure or well-trained staff, leading to a reduction in delays in diagnostics, and thus a quick start to treatment. Importantly, the latest WHO guidance recommends the usage of Xpert MTB/RIF and Xpert MTB/RIF Ultra, in order to detect TB and rifamicin-resistance [112]. The Xpert MTB/RIF test is an automatic, fast (<2 h) nucleic-acid-amplification assay, consisting of a single-use multi-chambered pre-prepared cartridge. According to the WHO, the test material may be the sputum samples, but other biological samples are also possible: cerebrospinal fluid, urine, pleural fluid, ascetic fluid, dialysis fluid, and pus. To perform the test, staff may only be minimally trained, and biosafety cabinets are not required. Therefore, tests may be available in most basic diagnostic-laboratories [113]. The sensitivity and specificity of the test is presented in the updated Cochrane Review. Total sensitivity and complete specificity were shown to be 85% and 98%, respectively; sensitivity compared to microscopic smear is 61%, while the sensitivity and specificity for the detection of rifampicin-resistance are 95% and 99%, respectively [114]. In turn, the Xpert MTB/RIF Ultra (Xpert Ultra) test is an improved version of the Xpert MTB/RIF, which utilizes a newly developed cartridge and software [115]. A comparison of the sensitivity and specificity of both tests revealed that Xpert Ultra has a higher sensitivity (90.9% (86.2 to 94.7) vs. 84.7% (78.6 to 89.9)), but a lower specificity (95.6% (93 0.0 to 97.4) vs. 98.4% (97.0 to 99.3)) than Xpert MTB/RIF. Conversely, a comparison of the sensitivity and specificity for the detection of rifampicin-resistance revealed similar results for both tests [114].

4.2. Viral Infection

The global COVID-19 pandemic highlighted the key role of the rapid and universal availability of diagnostics tests for viral diseases, where biosensors are widely used. On the one hand, the use of biosensors accelerates the possibility of isolating potentially positive-patients and the possibility of giving appropriate treatment, and, on the other hand, it improves the comfort of the patient awaiting the result [97].
According to the WHO report of 4 May 2022, there have been a total of over 511 million cases of COVID-19 worldwide. Despite the fact that from the end of March 2022 there has been a decreasing trend in new cases of COVID-19, apart from in the African and the Americas regions, the problem remains very serious. The gold standard recommended by the WHO for the diagnostic of SARS-CoV-2 causing COVID 19, remains real-time PCR. Real-time PCR and other nucleic-acid tests are highly precise, but are time-consuming and require specialized laboratories and highly trained personnel. In turn, immunoassays widely used in care settings have a much lower sensitivity [116]. For this reason, the pursuit of new diagnostic techniques is very desirable. The use of nanomaterial-based biosensors may be a new approach. Park et al. developed a surface-plasmon-resonance biosensor for the rapid diagnosis of SARS, using the fusion reaction of gold-binding polypeptides with the virus surface-antigen (SCVme). The detection limit of 200 ng/mL was demonstrated, and the test time was 10 min [117]. In turn, Murillo et al. presented a test based on interferometric optical-detection for the identification of specific anti-SARS-CoV-2 immunoglobulins in saliva and serum, for the direct detection of antibodies, and requiring no signal enhancers or chemical triggers [118]. A dual-functional plasmonic biosensor combining the plasmonic photothermal effect and localized surface plasmon resonance (LSPR) has been proposed as an alternative technique for the detection of selected SARS-CoV-2 sequences. This sensor is characterized by high sensitivity, with a detection limit of 0.22 pM [119]. In contrast, Seo et al. developed a field-effect transistor (FET) sensor using graphene sheets coated with a specific antibody against the SARS-CoV-2 spike protein. The LOD was estimated at 1.6 pfu/mL and 2.42 × 102 copies/mL for the culture medium and clinical samples, respectively [120]. Another biosensor proposal for SARS-CoV-2 screening is based on the detection of the SARS-CoV-2 S1 spike protein. This technique is characterized by a low detection limit: 1 fg/mL, a half-linear response range of 10 fg/mL–1 μg/mL, and a detection time of 3 min [121]. Similarly, the detection of the SARS-CoV-2 spike antigen was proposed by Krakus et al., and utilizes a colorimetric and electrochemical sensor based on gold nanoparticles. In the colorimetric method, as a result of contact with the antigen, gold nanoparticles changed their color from red to purple with a detection limit of 48 ng/mL. On the other hand, electrochemical detection was performed by spotting the probe solution on a disposable gold-electrode with screen printing, which enabled the detection of the SARS-CoV-2 antigen at the level of 1 pg/mL and a linear response to the antigen in the range of 1 pg/mL–10 ng/mL. Importantly, these techniques were specific to SARS-CoV-2, unlike the antigens of other pathogens including MERS COV, H1N1, and Streptococcus pneumoniae [122].
Despite reducing Haemophilus influenza infections as a result of the exacerbation of the COVID-19 pandemic, flu remains a serious problem. Hence, fast and critical diagnostics are still important. A label-free sensor that differentiates influenza A H1N1-subtypes (the seasonal and pandemic viruses: H1N1, H3N2 and 2009 H1N1) was developed by Bhardwaj et al. This biosensor relies on DNA aptamers by targeting the recombinant influenza-A-mini-hemagglutinin (mini-HA) protein. The sensitivity of this method (LOD) was found to be 3.7 plaque-forming units/mL [123]. In turn, Li et al. proposed a fluorescence sensor based on silver nanoparticles labeled with antibodies against H1N1. The LOD was estimated at 0.1 pg/mL, and the linear-detection range was 0.001–10 ng/mL [124]. Sensors detecting the serotype H5N1 were also designed [125,126]. The biosensor composed of a multi-functional DNA 3-way junction (3WJ) on a hollow Au spike-like nanoparticle (hAuSN) using an LSPR method, was presented by Lee et al. [125]. Meanwhile, Jiang et al. have developed a polydiacetylene-based biosensor for H5 influenza. The method of operation of this fast, sensitive (detection limit of 0.53 copies/μL) and specific biosensor, is to change the color from blue to red in the presence of the H5 virus. Moreover, using this technique it is possible to distinguish the H5 from the H3 influenza virus, the Newcastle disease virus and the porcine reproductive and respiratory syndrome virus [126].
The Ebola virus (EBOV) is an extremely virulent pathogen that causes epidemics of Ebola hemorrhagic fever, mainly in sub-Saharan African countries. In the years 2014–2016, EBOV spread to new areas, causing the largest epidemic of the disease in history and leading to the death of over 11,000 people. During this period, only 60% of cases were confirmed in the laboratory, which highlighted the need to seek for new fast and precise diagnostic tools [127]. An electrochemical DNA biosensor for the Ebola virus diagnosis has been proposed by Ilkhani and Farhad, the detection limit of which was 4.7 nM complementary oligonucleotides [128]. In turn, Baca et al. developed a surface-acoustic-wave biosensor for the Ebola virus that showed a limit of detection below the average level of viremia observed in the PCR test during the first day of symptomatic infection. A log-linear response was noted for highly fragmented Ebola viral-particles (detection limit—1.9 × 104 PFU/mL, prior to virus inactivation). Moreover, it was suggested that the sensor would be more sensitive to the infectious Ebola virus in its intact form [129].
Human immunodeficiency virus (HIV) infection is a chronic disease that progressively reduces the immunity of an infected person. Early and prompt diagnosis is key to reducing both mortality and spreading in the population. Optical- or electrochemical-biosensors currently available for clinical usage are based on reactions which recognize and bind molecules on the surface, including antigen–antibody reactions, nucleic acid hybridization, enzyme-cofactor, etc., as well as viral-titer monitoring in nanoscale strategies [130]. Gong et al. demonstrated a DNA biosensor for the detection of a fragment of the HIV-1 gene, based on a polyaniline/graphene nanocomposite with a lower detection limit of 0.1 fM and log-linear response of 0.1 fM–0.1 nM [131]. The label-free biosensor for detecting HIV-1 was also designed by Lee et al. In this strategy for direct determination, a probe modified with gold nanoparticles was used, on which an antibody fragment was immobilized, and different concentrations of HIV-1 virus-like particles with a detection limit in the range of 600 fg/mL–375 pg/mL [132] were used. Moreover, Shafiee et al. presented a free-label optic biosensor, based on the early capture and quantification of HIV-1 by nanostructured photonic crystals with a detection range of 104–108 copies/mL [133].
The Zika virus, belonging to the Flaviviridae family, genus Flavivirus, is mainly found in Africa and South America, and is listed by the WHO as a pathogen with high pandemic potential. It was initially considered to be harmless. However, the increased number of infants born with encephalopathy in mothers infected by the Zika virus has led to increased attention to it. Fearing an epidemic, the search for quick, cheap and readily available diagnostic tests was started [134]. An electrochemical biosensor composed of surface-printed polymers and graphene oxide compounds has been proposed by Tancharoen et al. This strategy implies a correlation between the electrical-signal titers and the virus concentration in the solution (buffer/serum). Importantly, the detection limit with this biosensor is characterized by similar values to the RT-PCR [135]. In turn, Kaushik et al. presented an electrochemical immunosensor detecting the Zika virus based on a functionalized interdigitated gold-microelectrode on which antibodies specific for the virus envelope protein (Zev-Abs) were immobilized. This technology showed selectivity in relation to ZEV-ABS, and high sensitivity (12 kΩM -1). The detection range was 10 pM-1 nM, with a detection limit of 10 pM [136]. In contrast, Faria et al. developed a portable, easy-to-use and low-cost immunosensor based on ZnO nanostructures immobilized with the ZIKV-NS1 antibody on a printed circuit board using cystamine and glutaraldehyde. This strategy showed high selectivity, with a linear-detection range of 0.1–100 ng/mL and a detection threshold below 1 pg/mL. The undoubted advantage of this strategy is the use of undiluted urine as the test material, and the lack of cross-reaction with the Dengue virus surface-antigen [137].
Infectious diseases, especially those endemic to poorer parts of the world, could be potential sources of future pandemics. In order to accelerate the correct diagnostics and, consequently, the treatment, it is extremely important to develop low-cost, portable diagnostic-technologies. However, the strategies we propose above represent only a small percentage of the needs, because there is still no research on biosensors that can be used for many dangerous diseases, which implies the need to continue research in this area.

5. Conclusions

Early detection of pathogens and toxins is essential and key for the rapid diagnosis and prevention of diseases. Various methods are widely used for their detection. Conventional laboratory-based methods such as plate culturing, ELISA and PCR techniques, remain dominant, but they have some disadvantages. As an alternative to conventional methods, the new approaches such as immunosensors have been developed and successfully applied in pathogen- and toxin-detection. Electrochemical, optical and piezoelectric immunosensors can detect pathogens such as Escherichia coli, Salmonella typhimurium, and Mycobacterium tuberculosis or toxins such as staphylococcal enterotoxin A, staphylococcal enterotoxin B, ricin, abrin, and botulinum neurotoxin, within minutes. Immunosensors possess great potential in becoming effective measurement tools, due to their real-time quantification, small sample-consumption, relatively low cost, and convenient instrument operation. It is believed that immunosensors will play a crucial role in the future pathogen and toxin sensor-detection.

Author Contributions

Conceptualization, M.B., M.S., M.C. and M.N.; supervision, M.B.; writing—original draft preparation, E.J.-K., M.N., N.C. and M.P.; writing—review and editing, M.B., M.N. and M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This paper has been supported by the European Union’s Horizon 2020 Research and Innovation Programme, under grant agreement No 101018596.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xu, S. Electromechanical Biosensors for Pathogen Detection. Microchim. Acta 2012, 178, 245–260. [Google Scholar] [CrossRef]
  2. Clark, G.C.; Casewell, N.R.; Elliott, C.T.; Harvey, A.L.; Jamieson, A.G.; Strong, P.N.; Turner, A.D. Friends or Foes? Emerging Impacts of Biological Toxins. Trends Biochem. Sci. 2019, 44, 365–379. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Balloux, F.; van Dorp, L. Q&A: What Are Pathogens, and What Have They Done to and for Us? BMC Biol. 2017, 15, 91. [Google Scholar] [CrossRef]
  4. Sharma, H.; Mutharasan, R. Review of Biosensors for Foodborne Pathogens and Toxins. Sens. Actuators B Chem. 2013, 183, 535–549. [Google Scholar] [CrossRef]
  5. Steffan, J.J.; Derby, J.A.; Brevik, E.C. Soil Pathogens That May Potentially Cause Pandemics, Including Severe Acute Respiratory Syndrome (Sars) Coronaviruses. Curr. Opin. Environ. Sci. Health 2020, 17, 35–40. [Google Scholar] [CrossRef]
  6. Cabral, J.P.S. Water Microbiology. Bacterial Pathogens and Water. Int. J. Environ. Res. Public Health 2010, 7, 3657–3703. [Google Scholar] [CrossRef]
  7. Lai, K.m.; Emberlin, J.; Colbeck, I. Outdoor Environments and Human Pathogens in Air. Environ. Health 2009, 8, S15. [Google Scholar] [CrossRef] [Green Version]
  8. Gerba, C.P. Environmentally Transmitted Pathogens. In Environmental Microbiology; Academic Press: San Diego, CA, USA, 2015; pp. 509–550. [Google Scholar] [CrossRef]
  9. van Seventer, J.M.; Hochberg, N.S. Principles of Infectious Diseases: Transmission, Diagnosis, Prevention, and control. In International Encyclopedia of Public Health; Elsevier: Amsterdam, The Netherlands, 2017; pp. 22–39. [Google Scholar] [CrossRef]
  10. Rajapaksha, P.; Elbourne, A.; Gangadoo, S.; Brown, R.; Cozzolino, D.; Chapman, J. A Review of Methods for the Detection of Pathogenic Microorganisms. Analyst 2019, 144, 396–411. [Google Scholar] [CrossRef]
  11. Wang, X.-H.; Wang, S. Sensors and Biosensors for the Determination of Small Molecule Biological Toxins. Sensors 2008, 8, 6045. [Google Scholar] [CrossRef]
  12. Dorner, B.G.; Rummel, A. Preface Biological Toxins—Ancient Molecules Posing a Current Threat; Multidisciplinary Digital Publishing Institute: Basel, Switzerland, 2015. [Google Scholar]
  13. Lazcka, O.; Campo, F.J.D.; Muñoz, F.X. Pathogen Detection: A Perspective of Traditional Methods and Biosensors. Biosens. Bioelectron. 2007, 22, 1205–1217. [Google Scholar] [CrossRef]
  14. Sun, J.; Huang, J.; Li, Y.; Lv, J.; Ding, X. A Simple and Rapid Colorimetric Bacteria Detection Method Based on Bacterial Inhibition of Glucose Oxidase-Catalyzed Reaction. Talanta 2019, 197, 304–309. [Google Scholar] [CrossRef] [PubMed]
  15. Priyanka, B.; Patil, R.K.; Dwarakanath, S. A Review on Detection Methods Used for Foodborne Pathogens. Indian J. Med. Res. 2016, 144, 327–338. [Google Scholar] [CrossRef] [PubMed]
  16. Leoni, E.; Legnani, P.P. Comparison of Selective Procedures for Isolation and Enumeration of Legionella Species from Hot Water Systems. J. Appl. Microbiol. 2001, 90, 27–33. [Google Scholar] [CrossRef] [PubMed]
  17. Hameed, S.; Xie, L.; Ying, Y. Conventional and Emerging Detection Techniques for Pathogenic Bacteria in Food Science: A Review. Trends Food Sci. Technol. 2018, 81, 61–73. [Google Scholar] [CrossRef]
  18. Song, C.; Liu, C.; Wu, S.; Li, H.; Guo, H.; Yang, B.; Qiu, S.; Li, J.; Liu, L.; Zeng, H.; et al. Development of a Lateral Flow Colloidal Gold Immunoassay Strip for the Simultaneous Detection of Shigella Boydii and Escherichia coli O157:H7 in Bread, Milk and Jelly Samples. Food Control 2016, 59, 345–351. [Google Scholar] [CrossRef]
  19. Kumar, S.; Balakrishna, K.; Batra, H.V. Enrichment-Elisa for Detection of Salmonella Typhi from Food and Water Samples. Biomed. Environ. Sci. 2008, 21, 137–143. [Google Scholar] [CrossRef] [PubMed]
  20. Kang, X.; Li, Y.; Fan, L.; Lin, F.; Wei, J.; Zhu, X.; Hu, Y.; Li, J.; Chang, G.; Zhu, Q.; et al. Development of an Elisa-Array for Simultaneous Detection of Five Encephalitis Viruses. Virol. J. 2012, 9, 56. [Google Scholar] [CrossRef] [Green Version]
  21. Liu, Y.; Cao, Y.; Wang, T.; Dong, Q.; Li, J.; Niu, C. Detection of 12 Common Food-Borne Bacterial Pathogens by Taqman Real-Time Pcr Using a Single Set of Reaction Conditions. Front. Microbiol. 2019, 10, 222. [Google Scholar] [CrossRef]
  22. Velusamy, V.; Arshak, K.; Korostynska, O.; Oliwa, K.; Adley, C. An Overview of Foodborne Pathogen Detection: In the Perspective of Biosensors. Biotechnol. Adv. 2010, 28, 232–254. [Google Scholar] [CrossRef]
  23. Johnson, G.; Nolan, T.; Bustin, S.A. Real-Time Quantitative Pcr, Pathogen Detection and Miqe. In PCR Detection of Microbial Pathogens; Springer: Berlin/Heidelberg, Germany, 2013; pp. 1–16. [Google Scholar]
  24. Alahi, M.E.E.; Mukhopadhyay, S.C. Detection Methodologies for Pathogen and Toxins: A Review. Sensors 2017, 17, 1885. [Google Scholar] [CrossRef] [Green Version]
  25. Sue, M.J.; Yeap, S.K.; Omar, A.R.; Tan, S.W. Application of Pcr-Elisa in Molecular Diagnosis. BioMed Res. Int. 2014, 2014, 653014. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Puppe, W.; Weigl, J.A.I.; Aron, G.; Gröndahl, B.; Schmitt, H.J.; Niesters, H.G.M.; Groen, J. Evaluation of a Multiplex Reverse Transcriptase Pcr Elisa for the Detection of Nine Respiratory Tract Pathogens. J. Clin. Virol. 2004, 30, 165–174. [Google Scholar] [CrossRef] [PubMed]
  27. Vidic, J.; Manzano, M.; Chang, C.-M.; Jaffrezic-Renault, N. Advanced Biosensors for Detection of Pathogens Related to Livestock and Poultry. Vet. Res. 2017, 48, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Ostrov, N.; Jimenez, M.; Billerbeck, S.; Brisbois, J.; Matragrano, J.; Ager, A.; Cornish, V.W. A Modular Yeast Biosensor for Low-Cost Point-of-Care Pathogen Detection. Sci. Adv. 2017, 3, e1603221. [Google Scholar] [CrossRef] [Green Version]
  29. Singh, R.; Mukherjee, M.D.; Sumana, G.; Gupta, R.K.; Sood, S.; Malhotra, B.D. Biosensors for Pathogen Detection: A Smart Approach towards Clinical Diagnosis. Sens. Actuators B Chem. 2014, 197, 385–404. [Google Scholar] [CrossRef]
  30. Chen, Y.; Qian, C.; Liu, C.; Shen, H.; Wang, Z.; Ping, J.; Wu, J.; Chen, H. Nucleic Acid Amplification Free Biosensors for Pathogen Detection. Biosens. Bioelectron. 2020, 153, 112049. [Google Scholar] [CrossRef]
  31. Perumal, V.; Hashim, U. Advances in Biosensors: Principle, Architecture and Applications. J. Appl. Biomed. 2014, 12, 1–15. [Google Scholar] [CrossRef]
  32. Ahmad, R.; Wolfbeis, O.S.; Hahn, Y.-B.; Alshareef, H.N.; Torsi, L.; Salama, K.N. Deposition of Nanomaterials: A Crucial Step in Biosensor Fabrication. Mater. Today Commun. 2018, 17, 289–321. [Google Scholar] [CrossRef]
  33. Mollarasouli, F.; Kurbanoglu, S.; Ozkan, S.A. The Role of Electrochemical Immunosensors in Clinical Analysis. Biosensors 2019, 9, 86. [Google Scholar] [CrossRef] [Green Version]
  34. Wu, J.; Fu, Z.; Yan, F.; Ju, H. Biomedical and Clinical Applications of Immunoassays and Immunosensors for Tumor Markers. TrAC Trends Anal. Chem. 2007, 26, 679–688. [Google Scholar] [CrossRef]
  35. Fowler, J.M.; Wong, D.K.Y.; Halsall, H.B.; Heineman, W.R. Chapter 5—Recent Developments in Electrochemical Immunoassays and Immunosensors. In Electrochemical Sensors, Biosensors and Their Biomedical Applications; Zhang, X., Ju, H., Wang, J., Eds.; Academic Press: San Diego, CA, USA, 2008; pp. 115–143. [Google Scholar]
  36. Felix, F.S.; Angnes, L. Electrochemical Immunosensors—A Powerful Tool for Analytical Applications. Biosens. Bioelectron. 2018, 102, 470–478. [Google Scholar] [CrossRef] [PubMed]
  37. Fang, L.; Liao, X.; Jia, B.; Shi, L.; Kang, L.; Zhou, L.; Kong, W. Recent Progress in Immunosensors for Pesticides. Biosens. Bioelectron. 2020, 164, 112255. [Google Scholar] [CrossRef] [PubMed]
  38. Daniels, J.S.; Pourmand, N. Label-Free Impedance Biosensors: Opportunities and Challenges. Electroanal. Int. J. Devoted Fundam. Pract. Asp. Electroanal. 2007, 19, 1239–1257. [Google Scholar] [CrossRef] [PubMed]
  39. de Castro, A.C.H.; Alves, L.M.; Siquieroli, A.C.S.; Madurro, J.M.; Brito-Madurro, A.G. Label-Free Electrochemical Immunosensor for Detection of Oncomarker Ca125 in Serum. Microchem. J. 2020, 155, 104746. [Google Scholar] [CrossRef]
  40. Luppa, P.B.; Sokoll, L.J.; Chan, D.W. Immunosensors—Principles and Applications to Clinical Chemistry. Clin. Chim. Acta 2001, 314, 1–26. [Google Scholar] [CrossRef]
  41. Lichtenberg, J.Y.; Ling, Y.; Kim, S. Non-Specific Adsorption Reduction Methods in Biosensing. Sensors 2019, 19, 2488. [Google Scholar] [CrossRef] [Green Version]
  42. Tang, J.; Tang, D.; Li, Q.; Su, B.; Qiu, B.; Chen, G. Sensitive Electrochemical Immunoassay of Carcinoembryonic Antigen with Signal Dual-Amplification Using Glucose Oxidase and an Artificial Catalase. Anal. Chim. Acta 2011, 697, 16–22. [Google Scholar] [CrossRef]
  43. Jiang, X.; Li, D.; Xu, X.; Ying, Y.; Li, Y.; Ye, Z.; Wang, J. Immunosensors for Detection of Pesticide Residues. Biosens. Bioelectron. 2008, 23, 1577–1587. [Google Scholar] [CrossRef]
  44. Zhang, Z.; Cong, Y.; Huang, Y.; Du, X. Nanomaterials-Based Electrochemical Immunosensors. Micromachines 2019, 10, 397. [Google Scholar] [CrossRef] [Green Version]
  45. Cho, I.-H.; Lee, J.; Kim, J.; Kang, M.-s.; Paik, J.K.; Ku, S.; Cho, H.-M.; Irudayaraj, J.; Kim, D.-H. Current Technologies of Electrochemical Immunosensors: Perspective on Signal Amplification. Sensors 2018, 18, 207. [Google Scholar] [CrossRef] [Green Version]
  46. Mahato, K.; Kumar, S.; Srivastava, A.; Maurya, P.K.; Singh, R.; Chandra, P. Chapter 14—Electrochemical Immunosensors: Fundamentals and Applications in Clinical Diagnostics. In Handbook of Immunoassay Technologies; Vashist, S.K., Luong, J.H.T., Eds.; Academic Press: San Diego, CA, USA, 2018; pp. 359–414. [Google Scholar]
  47. Diaconu, I.; Cristea, C.; Hârceagă, V.; Marrazza, G.; Berindan-Neagoe, I.; Săndulescu, R. Electrochemical Immunosensors in Breast and Ovarian Cancer. Clin. Chim. Acta 2013, 425, 128–138. [Google Scholar] [CrossRef] [PubMed]
  48. Liu, L.; Chao, Y.; Cao, W.; Wang, Y.; Luo, C.; Pang, X.; Fan, D.; Wei, Q. A Label-Free Amperometric Immunosensor for Detection of Zearalenone Based on Trimetallic Au-Core/Agpt-Shell Nanorattles and Mesoporous Carbon. Anal. Chim. Acta 2014, 847, 29–36. [Google Scholar] [CrossRef] [PubMed]
  49. Mikušová, Z.; Farka, Z.; Pastucha, M.; Poláchová, V.; Obořilová, R.; Skládal, P. Amperometric Immunosensor for Rapid Detection of Honeybee Pathogen Melissococcus Plutonius. Electroanalysis 2019, 31, 1969–1976. [Google Scholar] [CrossRef]
  50. Leonard, P.; Hearty, S.; Brennan, J.; Dunne, L.; Quinn, J.; Chakraborty, T.; O’Kennedy, R. Advances in Biosensors for Detection of Pathogens in Food and Water. Enzym. Microb. Technol. 2003, 32, 3–13. [Google Scholar] [CrossRef]
  51. Li, Y.; Cheng, P.; Gong, J.; Fang, L.; Deng, J.; Liang, W.; Zheng, J. Amperometric Immunosensor for the Detection of Escherichia coli O157:H7 in Food Specimens. Anal. Biochem. 2012, 421, 227–233. [Google Scholar] [CrossRef]
  52. Hiraiwa, M.; Kim, J.-H.; Lee, H.-B.; Inoue, S.; Becker, A.L.; Weigel, K.M.; Cangelosi, G.A.; Lee, K.-H.; Chung, J.-H. Amperometric Immunosensor for Rapid Detection of Mycobacterium Tuberculosis. J. Micromech. Microeng. Struct. Devices Syst. 2015, 25, 055013. [Google Scholar] [CrossRef] [Green Version]
  53. Lu, Y.; Liu, Y.; Zhao, Y.; Li, W.; Qiu, L.; Li, L. A Novel and Disposable Enzyme-Labeled Amperometric Immunosensor Based on Mwcnt Fibers for Listeria monocytogenes Detection. J. Nanomater. 2016, 2016, 3895920. [Google Scholar] [CrossRef] [Green Version]
  54. Qiu, J.-D.; Huang, H.; Liang, R.-P. Biocompatible and Label-Free Amperometric Immunosensor for Hepatitis B Surface Antigen Using a Sensing Film Composed of Poly(Allylamine)-Branched Ferrocene and Gold Nanoparticles. Microchim. Acta 2011, 174, 97. [Google Scholar] [CrossRef]
  55. Esteban-Fernández de Ávila, B.; Pedrero, M.; Campuzano, S.; Escamilla-Gómez, V.; Pingarrón, J.M. Sensitive and Rapid Amperometric Magnetoimmunosensor for the Determination of Staphylococcus Aureus. Anal. Bioanal. Chem. 2012, 403, 917–925. [Google Scholar] [CrossRef]
  56. Melo, A.M.A.; Furtado, R.F.; de Fatima Borges, M.; Biswas, A.; Cheng, H.N.; Alves, C.R. Performance of an Amperometric Immunosensor Assembled on Carboxymethylated Cashew Gum for Salmonella Detection. Microchem. J. 2021, 167, 106268. [Google Scholar] [CrossRef]
  57. Suresh, S.; Gupta, A.K.; Rao, V.K.; Om, k.; Vijayaraghavan, R. Amperometric Immunosensor for Ricin by Using on Graphite and Carbon Nanotube Paste Electrodes. Talanta 2010, 81, 703–708. [Google Scholar] [CrossRef] [PubMed]
  58. Silva, N.F.D.; Almeida, C.M.R.; Magalhães, J.M.C.S.; Gonçalves, M.P.; Freire, C.; Delerue-Matos, C. Development of a Disposable Paper-Based Potentiometric Immunosensor for Real-Time Detection of a Foodborne Pathogen. Biosens. Bioelectron. 2019, 141, 111317. [Google Scholar] [CrossRef] [PubMed]
  59. Silva, N.F.D.; Magalhães, J.M.C.S.; Barroso, M.F.; Oliva-Teles, T.; Freire, C.; Delerue-Matos, C. In Situ Formation of Gold Nanoparticles in Polymer Inclusion Membrane: Application as Platform in a Label-Free Potentiometric Immunosensor for Salmonella Typhimurium Detection. Talanta 2019, 194, 134–142. [Google Scholar] [CrossRef] [PubMed]
  60. Dill, K.; Stanker, L.H.; Young, C.R. Detection of Salmonella in Poultry Using a Silicon Chip-Based Biosensor. J. Biochem. Biophys. Methods 1999, 41, 61–67. [Google Scholar] [CrossRef] [PubMed]
  61. Sun, A.-L. A Potentiometric Immunosensor for Enterovirus 71 Based on Bis-Mpa-Cooh Dendrimer-Doped Agcl Nanospheres with a Silver Ion-Selective Electrode. Analyst 2018, 143, 487–492. [Google Scholar] [CrossRef] [PubMed]
  62. Gehring, A.G.; Patterson, D.L.; Tu, S.-I. Use of a Light-Addressable Potentiometric Sensor for the Detection Ofescherichia Colio157:H7. Anal. Biochem. 1998, 258, 293–298. [Google Scholar] [CrossRef] [PubMed]
  63. Yang, L.; Bashir, R. Electrical/Electrochemical Impedance for Rapid Detection of Foodborne Pathogenic Bacteria. Biotechnol. Adv. 2008, 26, 135–150. [Google Scholar] [CrossRef] [PubMed]
  64. Hayat, A.; Barthelmebs, L.; Marty, J.-L. Electrochemical Impedimetric Immunosensor for the Detection of Okadaic Acid in Mussel Sample. Sens. Actuators B Chem. 2012, 171-172, 810–815. [Google Scholar] [CrossRef]
  65. Varshney, M.; Li, Y.; Srinivasan, B.; Tung, S. A Label-Free, Microfluidics and Interdigitated Array Microelectrode-Based Impedance Biosensor in Combination with Nanoparticles Immunoseparation for Detection of Escherichia coli O157:H7 in Food Samples. Sens. Actuators B Chem. 2007, 128, 99–107. [Google Scholar] [CrossRef]
  66. Tan, F.; Leung, P.H.M.; Liu, Z.-b.; Zhang, Y.; Xiao, L.; Ye, W.; Zhang, X.; Yi, L.; Yang, M. A Pdms Microfluidic Impedance Immunosensor for E. coli O157:H7 and Staphylococcus Aureus Detection via Antibody-Immobilized Nanoporous Membrane. Sens. Actuators B Chem. 2011, 159, 328–335. [Google Scholar] [CrossRef]
  67. Nawaz, M.H.; Hayat, A.; Catanante, G.; Latif, U.; Marty, J.L. Development of a Portable and Disposable Ns1 Based Electrochemical Immunosensor for Early Diagnosis of Dengue Virus. Anal. Chim. Acta 2018, 1026, 1–7. [Google Scholar] [CrossRef] [PubMed]
  68. Chai, C.; Lee, J.; Takhistov, P. Direct Detection of the Biological Toxin in Acidic Environment by Electrochemical Impedimetric Immunosensor. Sensors 2010, 10, 11414–11427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Chai, C.; Takhistov, P. Label-Free Toxin Detection by Means of Time-Resolved Electrochemical Impedance Spectroscopy. Sensors 2010, 10, 655–669. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Hoa, D.T.; Kumar, T.N.S.; Punekar, N.S.; Srinivasa, R.S.; Lal, R.; Contractor, A.Q. A Biosensor Based on Conducting Polymers. Anal. Chem. 1992, 64, 2645–2646. [Google Scholar] [CrossRef]
  71. Chen, Z.-G. Conductometric Immunosensors for the Detection of Staphylococcal Enterotoxin B Based Bio-Electrocalytic Reaction on Micro-Comb Electrodes. Bioprocess Biosyst. Eng. 2008, 31, 345–350. [Google Scholar] [CrossRef]
  72. Tang, J.; Huang, J.; Su, B.; Chen, H.; Tang, D. Sandwich-Type Conductometric Immunoassay of Alpha-Fetoprotein in Human Serum Using Carbon Nanoparticles as Labels. Biochem. Eng. J. 2011, 53, 223–228. [Google Scholar] [CrossRef]
  73. Okafor, C.; Grooms, D.; Alocilja, E.; Bolin, S. Fabrication of a Novel Conductometric Biosensor for Detecting Mycobacterium Avium Subsp. Paratuberculosis Antibodies. Sensors 2008, 8, 6015. [Google Scholar] [CrossRef] [Green Version]
  74. Liu, H.; Yang, Y.; Chen, P.; Zhong, Z. Enhanced Conductometric Immunoassay for Hepatitis B Surface Antigen Using Double-Codified Nanogold Particles as Labels. Biochem. Eng. J. 2009, 45, 107–112. [Google Scholar] [CrossRef]
  75. Soldatkin, O.O.; Burdak, O.S.; Sergeyeva, T.A.; Arkhypova, V.M.; Dzyadevych, S.V.; Soldatkin, A.P. Acetylcholinesterase-Based Conductometric Biosensor for Determination of Aflatoxin B1. Sens. Actuators B Chem. 2013, 188, 999–1003. [Google Scholar] [CrossRef]
  76. Muhammad-Tahir, Z.; Alocilja, E.C. A Conductometric Biosensor for Biosecurity. Biosens. Bioelectron. 2003, 18, 813–819. [Google Scholar] [CrossRef]
  77. Hnaiein, M.; Hassen, W.M.; Abdelghani, A.; Fournier-Wirth, C.; Coste, J.; Bessueille, F.; Leonard, D.; Jaffrezic-Renault, N. A Conductometric Immunosensor Based on Functionalized Magnetite Nanoparticles for E. coli Detection. Electrochem. Commun. 2008, 10, 1152–1154. [Google Scholar] [CrossRef]
  78. Seydack, M. Nanoparticle Labels in Immunosensing Using Optical Detection Methods. Biosens. Bioelectron. 2005, 20, 2454–2469. [Google Scholar] [CrossRef] [PubMed]
  79. Lee, D.; Hwang, J.; Seo, Y.; Gilad, A.A.; Choi, J. Optical Immunosensors for the Efficient Detection of Target Biomolecules. Biotechnol. Bioprocess Eng. 2018, 23, 123–133. [Google Scholar] [CrossRef]
  80. Ogert, R.A.; Edward Brown, J.; Singh, B.R.; Shriver-Lake, L.C.; Ligler, F.S. Detection of Clostridium Botulinum Toxin a Using a Fiber Optic-Based Biosensor. Anal. Biochem. 1992, 205, 306–312. [Google Scholar] [CrossRef] [PubMed]
  81. Narang, U.; Anderson, G.P.; Ligler, F.S.; Burans, J. Fiber Optic-Based Biosensor for Ricin. Biosens. Bioelectron. 1997, 12, 937–945. [Google Scholar] [CrossRef] [PubMed]
  82. Geng, T.; Morgan Mark, T.; Bhunia Arun, K. Detection of Low Levels of Listeria Monocytogenes Cells by Using a Fiber-Optic Immunosensor. Appl. Environ. Microbiol. 2004, 70, 6138–6146. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Morlay, A.; Duquenoy, A.; Piat, F.; Calemczuk, R.; Mercey, T.; Livache, T.; Roupioz, Y. Label-Free Immuno-Sensors for the Fast Detection of Listeria in Food. Measurement 2017, 98, 305–310. [Google Scholar] [CrossRef]
  84. Masdor, N.A.; Altintas, Z.; Tothill, I.E. Surface Plasmon Resonance Immunosensor for the Detection of Campylobacter Jejuni. Chemosensors 2017, 5, 16. [Google Scholar] [CrossRef]
  85. Rasooly, A. Surface Plasmon Resonance Analysis of Staphylococcal Enterotoxin B in Food. J. Food Prot. 2001, 64, 37–43. [Google Scholar] [CrossRef]
  86. Oztuna, A.; Nazir, H.; Baysallar, M. Simultaneous Bacillus anthracis Spores Detection via Aminated-Poly(Vinyl Chloride) Coated Piezoelectric Crystal Immunosensor. J. Coat. 2014, 2014, 256168. [Google Scholar] [CrossRef] [Green Version]
  87. Friedlander, A.M.; Welkos, S.L.; Pitt, M.L.M.; Ezzell, J.W.; Worsham, P.L.; Rose, K.J.; Ivins, B.E.; Lowe, J.R.; Howe, G.B.; Mikesell, P. Postexposure Prophylaxis against Experimental Inhalation Anthrax. J. Infect. Dis. 1993, 167, 1239–1242. [Google Scholar] [CrossRef] [PubMed]
  88. Pohanka, M.; Skládal, P. Piezoelectric Immunosensor for Francisella Tularensis Detection Using Immunoglobulin M in a Limiting Dilution. Anal. Lett. 2005, 38, 411–422. [Google Scholar] [CrossRef]
  89. Xi-Hui, M.; Zhi-Qiang, Z.; Zhao-Yang, T.; Bing, L.; Lan-Qun, H. Detection of Abrin by Piezoelectric Immunosensor Based on Biotin-Avidin System. Chin. J. Anal. Chem. 2009, 37, 1499–1502. [Google Scholar]
  90. Salmain, M.; Ghasemi, M.; Boujday, S.; Spadavecchia, J.; Técher, C.; Val, F.; Le Moigne, V.; Gautier, M.; Briandet, R.; Pradier, C.-M. Piezoelectric Immunosensor for Direct and Rapid Detection of Staphylococcal Enterotoxin a (Sea) at the Ng Level. Biosens. Bioelectron. 2011, 29, 140–144. [Google Scholar] [CrossRef] [PubMed]
  91. Lin, H.-C.; Tsai, W.-C. Piezoelectric Crystal Immunosensor for the Detection of Staphylococcal Enterotoxin B. Biosens. Bioelectron. 2003, 18, 1479–1483. [Google Scholar] [CrossRef]
  92. Su, X.-L.; Li, Y. A Self-Assembled Monolayer-Based Piezoelectric Immunosensor for Rapid Detection of Escherichia coli O157:H7. Biosens. Bioelectron. 2004, 19, 563–574. [Google Scholar] [CrossRef]
  93. FIORMARKETS. Biosensors Market Size, Share & Trends Analysis Report by Technology (Thermal, Electrochemical, Optical), by Application (Medical, Agriculture), by End User (Poc Testing, Food Industry), by Region, and Segment Forecasts, 2022–2030. Available online: https://www.grandviewresearch.com/industry-analysis/biosensors-market (accessed on 7 December 2022).
  94. Madrid, R.E.; Ashur Ramallo, F.; Barraza, D.E.; Chaile, R.E. Smartphone-Based Biosensor Devices for Healthcare: Technologies, Trends, and Adoption by End-Users. Bioengineering 2022, 9, 101. [Google Scholar] [CrossRef]
  95. Nayak, S.; Blumenfeld, N.R.; Laksanasopin, T.; Sia, S.K. Point-of-Care Diagnostics: Recent Developments in a Connected Age. Anal. Chem. 2017, 89, 102–123. [Google Scholar] [CrossRef] [Green Version]
  96. Wilson, S.; Bohn, M.K.; Adeli, K. Poct: An Inherently Ideal Tool in Pediatric Laboratory Medicine. Ejifcc 2021, 32, 145–157. [Google Scholar]
  97. Pohanka, M. Progress in Biosensors for the Point-of-Care Diagnosis of COVID-19. Sensors 2022, 22, 7423. [Google Scholar] [CrossRef]
  98. Jones, K.E.; Patel, N.G.; Levy, M.A.; Storeygard, A.; Balk, D.; Gittleman, J.L.; Daszak, P. Global Trends in Emerging Infectious Diseases. Nature 2008, 451, 990–993. [Google Scholar] [CrossRef] [PubMed]
  99. Chen, J.; Andler, S.M.; Goddard, J.M.; Nugen, S.R.; Rotello, V.M. Integrating Recognition Elements with Nanomaterials for Bacteria Sensing. Chem. Soc. Rev. 2017, 46, 1272–1283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  100. Abushaheen, M.A.; Fatani, A.J.; Alosaimi, M.; Mansy, W.; George, M.; Acharya, S.; Rathod, S.; Divakar, D.D.; Jhugroo, C.; Vellappally, S.; et al. Antimicrobial Resistance, Mechanisms and Its Clinical Significance. Dis. Mon. 2020, 66, 100971. [Google Scholar] [CrossRef]
  101. Karaman, R.; Jubeh, B.; Breijyeh, Z. Resistance of Gram-Positive Bacteria to Current Antibacterial Agents and Overcoming Approaches. Molecules 2020, 25, 2888. [Google Scholar] [CrossRef] [PubMed]
  102. Jevons, M.P. “Celbenin”—Resistant Staphylococci. Br. Med. J. 1961, 1, 124–125. [Google Scholar] [CrossRef]
  103. Hernández, R.; Vallés, C.; Benito, A.M.; Maser, W.K.; Rius, F.X.; Riu, J. Graphene-Based Potentiometric Biosensor for the Immediate Detection of Living Bacteria. Biosens. Bioelectron. 2014, 54, 553–557. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  104. Ahari, H.; Hedayati, M.; Akbari-adergani, B.; Kakoolaki, S.; Hosseini, H.; Anvar, A. Staphylococcus Aureus Exotoxin Detection Using Potentiometric Nanobiosensor for Microbial Electrode Approach with the Effects of Ph and Temperature. Int. J. Food Prop. 2017, 20, 1578–1587. [Google Scholar] [CrossRef] [Green Version]
  105. Suaifan, G.A.; Alhogail, S.; Zourob, M. Rapid and Low-Cost Biosensor for the Detection of Staphylococcus Aureus. Biosens. Bioelectron. 2017, 90, 230–237. [Google Scholar] [CrossRef]
  106. Ngamsom, B.; Truyts, A.; Fourie, L.; Kumar, S.; Tarn, M.D.; Iles, A.; Moodley, K.; Land, K.J.; Pamme, N. A Microfluidic Device for Rapid Screening of E. coli O157:H7 Based on Ifast and Atp Bioluminescence Assay for Water Analysis. Chemistry 2017, 23, 12754–12757. [Google Scholar] [CrossRef]
  107. Cui, X.; Huang, Y.; Wang, J.; Zhang, L.; Rong, Y.; Lai, W.; Chen, T. A Remarkable Sensitivity Enhancement in a Gold Nanoparticle-Based Lateral Flow Immunoassay for the Detection of Escherichia coli O157:H7. RSC Adv. 2015, 5, 45092–45097. [Google Scholar] [CrossRef]
  108. Wan, J.; Ai, J.; Zhang, Y.; Geng, X.; Gao, Q.; Cheng, Z. Signal-Off Impedimetric Immunosensor for the Detection of Escherichia coli O157:H7. Sci. Rep. 2016, 6, 19806. [Google Scholar] [CrossRef] [PubMed]
  109. Xiao, R.; Rong, Z.; Long, F.; Liu, Q. Portable Evanescent Wave Fiber Biosensor for Highly Sensitive Detection of Shigella. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 2014, 132, 1–5. [Google Scholar] [CrossRef] [PubMed]
  110. Elahi, N.; Baghersad, M.H.; Kamali, M. Precise, Direct, and Rapid Detection of Shigella Spa Gene by a Novel Unmodified Aunps-Based Optical Genosensing System. J. Microbiol. Methods 2019, 162, 42–49. [Google Scholar] [CrossRef] [PubMed]
  111. Jeremiah, C.; Petersen, E.; Nantanda, R.; Mungai, B.N.; Migliori, G.B.; Amanullah, F.; Lungu, P.; Ntoumi, F.; Kumarasamy, N.; Maeurer, M.; et al. The Who Global Tuberculosis 2021 Report—Not So Good News and Turning the Tide Back to End Tb. Int. J. Infect. Dis. 2022, 124, S26–S29. [Google Scholar] [CrossRef]
  112. WHO. Who Consolidated Guidelines on Tuberculosis: Module 3: Diagnosis—Rapid Diagnostics for Tuberculosis Detection [Internet]; World Health Organization: Geneva, Switzerland, 2021. [Google Scholar]
  113. Haraka, F.; Kakolwa, M.; Schumacher, S.G.; Nathavitharana, R.R.; Denkinger, C.M.; Gagneux, S.; Reither, K.; Ross, A. Impact of the Diagnostic Test Xpert Mtb/Rif on Patient Outcomes for Tuberculosis. Cochrane Database Syst. Rev. 2021, 5, Cd012972. [Google Scholar] [CrossRef]
  114. Zifodya, J.S.; Kreniske, J.S.; Schiller, I.; Kohli, M.; Dendukuri, N.; Schumacher, S.G.; Ochodo, E.A.; Haraka, F.; Zwerling, A.A.; Pai, M.; et al. Xpert Ultra Versus Xpert Mtb/Rif for Pulmonary Tuberculosis and Rifampicin Resistance in Adults with Presumptive Pulmonary Tuberculosis. Cochrane Database Syst. Rev. 2021, 2, Cd009593. [Google Scholar] [CrossRef]
  115. Banerjee, S.; Severn, M. Cadth Rapid Response Reports. In Rapid and Simultaneous Tuberculosis and Antibiotic Susceptibility Testing for the Diagnosis of Pulmonary Tuberculosis and Rifampicin Resistance: A Review of Diagnostic Accuracy; Canadian Agency for Drugs and Technologies in Health Copyright © 2022 Canadian Agency for Drugs and Technologies in Health: Ottawa, ON, Canada, 2020. [Google Scholar]
  116. World Health Organization, E.R. Weekly Epidemiological Update on COVID-19—4 May 2022; World Health Organization: Genewa, Switzerland, 2022. [Google Scholar]
  117. Park, T.J.; Hyun, M.S.; Lee, H.J.; Lee, S.Y.; Ko, S. A Self-Assembled Fusion Protein-Based Surface Plasmon Resonance Biosensor for Rapid Diagnosis of Severe Acute Respiratory Syndrome. Talanta 2009, 79, 295–301. [Google Scholar] [CrossRef]
  118. Murillo, A.M.M.; Tomé-Amat, J.; Ramírez, Y.; Garrido-Arandia, M.; Valle, L.G.; Hernández-Ramírez, G.; Tramarin, L.; Herreros, P.; Santamaría, B.; Díaz-Perales, A.; et al. Developing an Optical Interferometric Detection Method Based Biosensor for Detecting Specific SARS-CoV-2 Immunoglobulins in Serum and Saliva, and Their Corresponding Elisa Correlation. Sens. Actuators B Chem. 2021, 345, 130394. [Google Scholar] [CrossRef]
  119. Qiu, G.; Gai, Z.; Tao, Y.; Schmitt, J.; Kullak-Ublick, G.A.; Wang, J. Dual-Functional Plasmonic Photothermal Biosensors for Highly Accurate Severe Acute Respiratory Syndrome Coronavirus 2 Detection. ACS Nano 2020, 14, 5268–5277. [Google Scholar] [CrossRef] [Green Version]
  120. Seo, G.; Lee, G.; Kim, M.J.; Baek, S.H.; Choi, M.; Ku, K.B.; Lee, C.S.; Jun, S.; Park, D.; Kim, H.G.; et al. Rapid Detection of COVID-19 Causative Virus (SARS-CoV-2) in Human Nasopharyngeal Swab Specimens Using Field-Effect Transistor-Based Biosensor. ACS Nano 2020, 14, 5135–5142. [Google Scholar] [CrossRef]
  121. Mavrikou, S.; Moschopoulou, G.; Tsekouras, V.; Kintzios, S. Development of a Portable, Ultra-Rapid and Ultra-Sensitive Cell-Based Biosensor for the Direct Detection of the SARS-CoV-2 S1 Spike Protein Antigen. Sensors 2020, 20, 3121. [Google Scholar] [CrossRef]
  122. Karakuş, E.; Erdemir, E.; Demirbilek, N.; Liv, L. Colorimetric and Electrochemical Detection of SARS-CoV-2 Spike Antigen with a Gold Nanoparticle-Based Biosensor. Anal. Chim. Acta 2021, 1182, 338939. [Google Scholar] [CrossRef]
  123. Bhardwaj, J.; Chaudhary, N.; Kim, H.; Jang, J. Subtyping of Influenza a H1n1 Virus Using a Label-Free Electrochemical Biosensor Based on the DNA Aptamer Targeting the Stem Region of Ha Protein. Anal. Chim. Acta 2019, 1064, 94–103. [Google Scholar] [CrossRef]
  124. Li, Y.; Hong, M.; Qiu, B.; Lin, Z.; Chen, Y.; Cai, Z.; Chen, G. Highly Sensitive Fluorescent Immunosensor for Detection of Influenza Virus Based on Ag Autocatalysis. Biosens. Bioelectron. 2014, 54, 358–364. [Google Scholar] [CrossRef]
  125. Lee, T.; Kim, G.H.; Kim, S.M.; Hong, K.; Kim, Y.; Park, C.; Sohn, H.; Min, J. Label-Free Localized Surface Plasmon Resonance Biosensor Composed of Multi-Functional DNA 3 Way Junction on Hollow Au Spike-Like Nanoparticles (Hausn) for Avian Influenza Virus Detection. Colloids Surf. B Biointerfaces 2019, 182, 110341. [Google Scholar] [CrossRef]
  126. Jiang, L.; Luo, J.; Dong, W.; Wang, C.; Jin, W.; Xia, Y.; Wang, H.; Ding, H.; He, H. Development and Evaluation of a Polydiacetylene Based Biosensor for the Detection of H5 Influenza Virus. J. Virol. Methods 2015, 219, 38–45. [Google Scholar] [CrossRef] [PubMed]
  127. Tembo, J.; Simulundu, E.; Changula, K.; Handley, D.; Gilbert, M.; Chilufya, M.; Asogun, D.; Ansumana, R.; Kapata, N.; Ntoumi, F.; et al. Recent Advances in the Development and Evaluation of Molecular Diagnostics for Ebola Virus Disease. Expert Rev. Mol. Diagn. 2019, 19, 325–340. [Google Scholar] [CrossRef]
  128. Ilkhani, H.; Farhad, S. A Novel Electrochemical DNA Biosensor for Ebola Virus Detection. Anal. Biochem. 2018, 557, 151–155. [Google Scholar] [CrossRef] [PubMed]
  129. Baca, J.T.; Severns, V.; Lovato, D.; Branch, D.W.; Larson, R.S. Rapid Detection of Ebola Virus with a Reagent-Free, Point-of-Care Biosensor. Sensors 2015, 15, 8605–8614. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  130. Farzin, L.; Shamsipur, M.; Samandari, L.; Sheibani, S. Hiv Biosensors for Early Diagnosis of Infection: The Intertwine of Nanotechnology with Sensing Strategies. Talanta 2020, 206, 120201. [Google Scholar] [CrossRef]
  131. Gong, Q.; Han, H.; Yang, H.; Zhang, M.; Sun, X.; Liang, Y.; Liu, Z.; Zhang, W.; Qiao, J. Sensitive Electrochemical DNA Sensor for the Detection of Hiv Based on a Polyaniline/Graphene Nanocomposite. J. Mater. 2019, 5, 313–319. [Google Scholar] [CrossRef]
  132. Lee, J.-H.; Oh, B.-K.; Choi, J.-W. Electrochemical Sensor Based on Direct Electron Transfer of Hiv-1 Virus at Au Nanoparticle Modified Ito Electrode. Biosens. Bioelectron. 2013, 49, 531–535. [Google Scholar] [CrossRef] [PubMed]
  133. Shafiee, H.; Lidstone, E.A.; Jahangir, M.; Inci, F.; Hanhauser, E.; Henrich, T.J.; Kuritzkes, D.R.; Cunningham, B.T.; Demirci, U. Nanostructured Optical Photonic Crystal Biosensor for Hiv Viral Load Measurement. Sci. Rep. 2014, 4, 4116. [Google Scholar] [CrossRef] [Green Version]
  134. Hasan, S.; Saeed, S.; Panigrahi, R.; Choudhary, P. Zika Virus: A Global Public Health Menace: A Comprehensive Update. J. Int. Soc. Prev. Community Dent. 2019, 9, 316–327. [Google Scholar] [CrossRef] [PubMed]
  135. Tancharoen, C.; Sukjee, W.; Thepparit, C.; Jaimipuk, T.; Auewarakul, P.; Thitithanyanont, A.; Sangma, C. Electrochemical Biosensor Based on Surface Imprinting for Zika Virus Detection in Serum. ACS Sens. 2019, 4, 69–75. [Google Scholar] [CrossRef]
  136. Kaushik, A.; Yndart, A.; Kumar, S.; Jayant, R.D.; Vashist, A.; Brown, A.N.; Li, C.-Z.; Nair, M. A Sensitive Electrochemical Immunosensor for Label-Free Detection of Zika-Virus Protein. Sci. Rep. 2018, 8, 9700. [Google Scholar] [CrossRef] [Green Version]
  137. Faria, A.M.; Mazon, T. Early Diagnosis of Zika Infection Using a Zno Nanostructures-Based Rapid Electrochemical Biosensor. Talanta 2019, 203, 153–160. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Basic principle and biosensors components.
Figure 1. Basic principle and biosensors components.
Sensors 22 09757 g001
Figure 2. Schematic diagram of electrochemical immunosensor.
Figure 2. Schematic diagram of electrochemical immunosensor.
Sensors 22 09757 g002
Figure 3. Schematic general principle of optical immunosensors.
Figure 3. Schematic general principle of optical immunosensors.
Sensors 22 09757 g003
Figure 4. Schematic diagram of piezoelectric immunosensor.
Figure 4. Schematic diagram of piezoelectric immunosensor.
Sensors 22 09757 g004
Table 1. Application of amperometric immunosensor for pathogen detection.
Table 1. Application of amperometric immunosensor for pathogen detection.
Type of ImmunosensorDetected PathogenLODReference
Electrochemical (Amperometric)Escherichia coli O157:H72.5 × 102 CFU/mL[51]
Mycobacterium tuberculosis1 × 102 CFU/mL[52]
Listeria monocytogenes1.07 × 102 CFU/mL[53]
Hepatitis B virus40 pg/mL[54]
Staphylococcus aureus1 CFU/mL[55]
Salmonella typhimurium10 CFU/mL[56]
Ricin10 ng/mL[57]
Table 2. Application of potentiometric immunosensor for pathogen detection.
Table 2. Application of potentiometric immunosensor for pathogen detection.
Type of ImmunosensorDetected PathogenLODReference
Electrochemical (Potentiometric)Salmonella typhimurium5 cells/mL[58]
S. typhimurium6 cells/mL[59]
S. typhimurium1.19 × 102 CFU/mL[60]
Enterovirus 710.058 ng/mL[61]
Escherichia coli O157:H77.1 × 102 cells/mL[62]
Table 3. Application of impedimetric immunosensor for pathogens detection.
Table 3. Application of impedimetric immunosensor for pathogens detection.
Type of ImmunosensorDetected PathogenLODReference
Electrochemical (Impedimetric)E. coli O157:H71.6 × 102 in pure culture
1.2 × 103 cells
[65]
Staphylococcus aureus1 × 102 CFU/mL[66]
Dengue virus0.3 ng/mL[67]
Ricin500 ng/mL[68]
Staphylococcal enterotoxin B10 pg/mL[69]
Table 4. Conductometric immunosensor in pathogen identification.
Table 4. Conductometric immunosensor in pathogen identification.
Type of ImmunosensorDetected PathogenLODReference
Electrochemical (Conductometric)Hepatitis B virus0.01 ng/mL[74]
Aflatoxin B10.05 μg/ml[75]
E. coli O157:H779 CFU/mL[76]
Salmonella spp.83 CFU/mL of
E. coli0.5 CFU/mL[77]
Table 5. Applications of optical immunosensors in the detection of different toxins and bacteria.
Table 5. Applications of optical immunosensors in the detection of different toxins and bacteria.
Type of ImmunosensorDetected PathogenLODReference
OpticalClostridium botulinum toxin5 ng/mL[80]
Ricin1 ng/mL[81]
L. monocytogenes4.3 × 103 CFU/mL[82]
L. monocytogenesn/a[83]
Campylobacter jejuni4 × 104 CFU/mL[84]
Staphylococcal enterotoxin B10 ng/ml[85]
Table 6. Summary of research concerning the use of immunosensors in the detection of various pathogens and toxins.
Table 6. Summary of research concerning the use of immunosensors in the detection of various pathogens and toxins.
Type of ImmunosensorDetected PathogenLODReference
PiezoelectricBacillus anthracis spores2187 spores[86]
Francisella tularensis1 × 105 CFU/mL[88]
Abrin0.05 mg/L[89]
Staphylococcal enterotoxin A7 ng/mL[90]
Staphylococcal enterotoxin B2.5 µg/mL[91]
Escherichia coli O157:H7103 CFU/mL[92]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Janik-Karpinska, E.; Ceremuga, M.; Niemcewicz, M.; Podogrocki, M.; Stela, M.; Cichon, N.; Bijak, M. Immunosensors—The Future of Pathogen Real-Time Detection. Sensors 2022, 22, 9757. https://doi.org/10.3390/s22249757

AMA Style

Janik-Karpinska E, Ceremuga M, Niemcewicz M, Podogrocki M, Stela M, Cichon N, Bijak M. Immunosensors—The Future of Pathogen Real-Time Detection. Sensors. 2022; 22(24):9757. https://doi.org/10.3390/s22249757

Chicago/Turabian Style

Janik-Karpinska, Edyta, Michal Ceremuga, Marcin Niemcewicz, Marcin Podogrocki, Maksymilian Stela, Natalia Cichon, and Michal Bijak. 2022. "Immunosensors—The Future of Pathogen Real-Time Detection" Sensors 22, no. 24: 9757. https://doi.org/10.3390/s22249757

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop