Quartz-Crystal Microbalance (QCM) for Public Health

CHAPTER SIX

Quartz-Crystal Microbalance (QCM) for Public Health: An Overview of Its Applications Nicola Luigi Bragazzi*, Daniela Amicizia*, Donatella Panatto*, Daniela Tramalloni*, Ivana Valle†, Roberto Gasparini*,1 *Department of Health Sciences (DISSAL), Via Antonio Pastore 1, University of Genoa, Genoa, Italy † SSD “Popolazione a rischio,” Health Prevention Department, Local Health Unit ASL3 Genovese, Genoa, Italy 1 Corresponding author: e-mail address: [email protected]

Contents 1. 2. 3. 4. 5. 6. 7. 8.

9. 10. 11. 12. 13. 14. 15. 16.

17.

Introduction Biosensors and Nanosensors Quartz-Crystal Microbalance Bacteria and QCM: A New Strategy for Detecting Microbial Population QCM and Nosocomial Infections Bacteria and QCM: Role in Monitoring Antimicrobial Susceptibility and Drug Resistance Influenza Virus and Other Respiratory Viruses Sexually Transmitted Diseases 8.1 Viral Hepatitis 8.2 Chlamydia 8.3 HIV/AIDS 8.4 Herpes Simplex Virus Invasive Diseases Tropical and Tropical Neglected Diseases Cancer Chronic Degenerative Diseases Occupational Hygiene Water Safety Veterinary Public Health Food Hygiene 16.1 Food-borne pathogens 16.2 Mycotoxins 16.3 Allergens 16.4 Pesticides and Other Chemical Components 16.5 Micronutrients 16.6 Genetically Modified Organisms Environmental Monitoring

Advances in Protein Chemistry and Structural Biology, Volume 101 ISSN 1876-1623 http://dx.doi.org/10.1016/bs.apcsb.2015.08.002

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18. Biohazards and Bioterrorism 19. Homeland Security 20. Concluding Remarks References

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Abstract Nanobiotechnologies, from the convergence of nanotechnology and molecular biology and postgenomics medicine, play a major role in the field of public health. This overview summarizes the potentiality of piezoelectric sensors, and in particular, of quartz-crystal microbalance (QCM), a physical nanogram-sensitive device. QCM enables the rapid, real time, on-site detection of pathogens with an enormous burden in public health, such as influenza and other respiratory viruses, hepatitis B virus (HBV), and drugresistant bacteria, among others. Further, it allows to detect food allergens, food-borne pathogens, such as Escherichia coli and Salmonella typhimurium, and food chemical contaminants, as well as water-borne microorganisms and environmental contaminants. Moreover, QCM holds promises in early cancer detection and screening of new antiblastic drugs. Applications for monitoring biohazards, for assuring homeland security, and preventing bioterrorism are also discussed.

1. INTRODUCTION Recent advances in the field of molecular biology and postgenomics medicine, as well as in the field of nanotechnology enabling the characterization, fabrication, and manipulation of materials down to the nanoscale, have allowed the design and the development of nanobiotechnologies, such as quantum dots, magnetic and gold nanoparticles, nanowires, nanorods, nanocarriers, and carbon nanotubes (both single walled and multiwalled, SWNTs, and MWNTs). Nanobiomaterials-based sensors are characterized by amplified signals, enhanced precision, specificity, sensitivity, and selectivity with respect to classical sensors. Moreover, they exhibit improved detection time with increased detection limits and reproducibility. Being miniaturized and scalable, they have increased portability and lower costs. Nanobiotechnologies-based sensors play a major role in the field of public health, in that they can detect also low levels of pathogens and therefore enable an effective monitoring, surveillance and control of infections (Qasim, Lim, Park, & Na, 2014). In particular, they represent sensitive assays for pathogen sensing in complex matrices, such as water, food, and environmental samples (Gilmartin & O’Kennedy, 2012; Li & Sheng, 2014; Petrinca et al., 2009; Virolainen & Karp, 2014). Further, nanobiotechnologies hold

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great promises in that some of them have biocidal properties, such as zinc oxide (ZnO) or silver nanoparticles, even if some safety and environmental concerns may limit their usage (Petrinca et al., 2009). This overview will be specifically focused on the extant applications and future prospects of nanobiotechnology-based biosensors, and in particular of piezoelectric mass-sensing devices, such as the quartz-crystal microbalance (QCM), in the field of public health.

2. BIOSENSORS AND NANOSENSORS A biosensor is a small, self-contained, highly integrated bioanalytical device, which enables the detection of a target analyte. It combines a biological recognition component with a physical, chemical, or physicochemical detector (termed also as transducer) (Heller, 1996; Keiser, Xiong, Cui, & Shum, 2014; Koncki, 2007; Ramanathan & Danielsson, 2001; Wang, 2006). The analyte can be of different types (ion, gas, oligonucleotides, or proteins) (The´venot, Toth, Durst, & Wilson, 2001), as well as the type of transducer (optical, mechanical or mass sensing, thermal or calorimetric or thermometric, chemical/electrochemical/electrical). Electrochemical and optical biosensors are particularly common. Optical biosensors (Poeggel et al., 2015) make use of colorimetric devices, optic fibers, planar guide waves, interferometer, surface plasmon resonance, Raman and nanomaterials that measure color changes/reflectance, fluorescence and phosphorescence, absorbance (or light absorption), luminescence (chemiluminescence/bioluminescence) emissions, that occur in the ultraviolet, visible, or near-infrared spectral regions, Raman scattering, and refractive index, respectively. Electrochemical biosensors can be subdivided into four main categories according to the type of measurement: amperometric, conductometric/ impedimetric, or potentiometric. They can be based on electrochemical impedance spectroscopy, cyclic voltammetry, or anodic stripping voltammetry. They can make use of coated-wired electrodes, field-effect transistors (FETs), ion-selective electrodes, ion-selective FETs (ISFETs), microelectrodes, interdigitated electrodes, chemically modified electrodes (Nirschl, Reuter, & V€ or€ os, 2011; Stradiotto, Yamanaka, & Zanoni, 2003; The´venot et al., 2001). In the specific field of biosensors, FETs/ISFETs are usually further modified, being coupled with biological components,

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such as enzymes—thus becoming enzyme FETs—or immunological molecules—thus becoming immunological FETs (The´venot et al., 2001). Calorimetric sensors measure absorption or evolution of heat, as a change in the temperature within the reaction medium. They commonly make use of thermistors, which in the specific field of biosensors are usually further modified and coupled with biological components, like enzymes—thus becoming enzyme thermistors (Ramanathan & Danielsson, 2001). Other transducers include Peltier elements, Darlington transistors, and thermopiles. Thermometric sensors are usually based on flow injection analysis technique (Ramanathan & Danielsson, 2001). Closely related to thermal sensors, are the pyroelectric sensors, which exploit the pyroelectric effect, which is the generation of surface charges during the change of spontaneous polarization with temperature, and the pyro-optical devices (Cuadras, Gasulla, & Ferrari, 2010). The sensing biological component is a biologically derived material or biomimetic component that interacts with the analyte, in a biochemical/ biophysical way, binding it or recognizing it. Finally, biosensor is connected to a PC or any reader device, enabling the display of the signals, which can be further processed and analyzed. Therefore, biosensors uniquely combine physics and chemistry, biophysics and biochemistry, functional/structural biology, information science, and electronics. Biosensors can be roughly subdivided into bioaffinity or biocomplexing and biocatalytic (Garcı´a-Martinez et al., 2011; Murugaiyan, Ramasamy, Gopal, & Kuzhandaivelu, 2014). They can be also classified as probing biosensors or reacting biosensors. Simplifying, a biocomplexing biosensor is a probing device, while a biocatalytic one is reacting. Furthermore, they can be subdivided into first generation, second generation (sensors with a mediator, which facilitates the reaction), and third generation (reagentless sensors, in which a direct electron transfer occurs) (Murugaiyan et al., 2014). Biosensors and nanosensors play a major role in nanomedicine Nicolini, Adami, et al., 2012; Nicolini, Bragazzi, & Pechkova, 2012), being particularly useful for biomarker detection (Swierczewska, Liu, Lee, & Chen, 2012), medical diagnosis in general and in particular cancer clinical testing (Spera et al., 2013), pathogen detection, analysis of environmental samples, and food/water safety assurance (Hong, Li, & Li, 2012), as we will see in the following paragraphs.

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3. QUARTZ-CRYSTAL MICROBALANCE A QCM is a physical, nanogram-sensitive device that is able to detect variations in the resonance frequency (Δf ) of an electrically driven quartz crystal with changes in thickness or mass per unit area (Δm), when adding (loaded quartz) or removing (unloaded quartz) small masses, or when a given phenomenon occurs (for example, oxide growth/decay, thin film growth/ deposition, a biochemical interaction, or a catalysis at the surface of the quartz) (Figs. 1 and 2). Quartz is an acoustic resonator and experiences the piezoelectric effect, that is to say applying alternating current to the quartz

Figure 1 A quartz crystal with gold electrodes, as the core component of the quartzcrystal microbalance (QCM) sensor. When the electrodes are connected to an oscillator and voltage is applied, the quartz crystal begins to oscillate at its resonance frequency (fundamental frequency) due to the piezoelectric effect.

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Time

Figure 2 Pictorial representation of the mechanism of the quartz-crystal microbalance (QCM) biosensor. The quartz crystal has been coated with antibodies (QCM-based immunosensor). When they bind the target analyte, the frequency of oscillation is perturbed and tends to decrease throughout time. It is possible to study in details the adsorption kinetics.

crystal induces acoustic waves. QCM is known also as thickness shear mode resonator or bulk acoustic wave transducer. Other types of piezoelectric sensors include flexural plate wave resonator, surface acoustic wave resonator, and shear horizontal acoustic plate mode resonator, among others (Buck, Lindner, Kutner, & Inzelt, 2004; Cooper & Singleton, 2007). Under some assumptions (termed as small load assumptions), this frequency variation can be approximately quantified and correlated to the mass change using the well-known Sauerbrey’s equation: Δf ¼ Cf  Δm where Δf is the change in frequency (Hz), Cf is the sensitivity factor of the used crystal quartz (for example, 56.6 Hz cm2/μg for a 5 MHz AT-cut quartz crystal at room temperature), and Δm is the change in mass per unit area (Rodahl & Kasemo, 1996). Sensitivity factor can be computed as: 2  n  f02 Cf ¼  1=2 μ q ρq where μq is the shear modulus of quartz (2.947  1011 g/cm s2), ρq is the quartz density (2.648 g/cm3), n is the harmonic number at which the crystal

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is driven, and f0 is the fundamental resonant frequency of the quartz crystal (Dixon, 2008). The dissipation factor can be computed as the inverse of the quality factor of the resonance, D¼

1 w ¼ Q f0

where w is the bandwidth; the formula quantifies the damping in the system. It can also be computed as (Dixon, 2008): D¼

1 πΔf τ

where Δf is the change in frequency (Hz) and τ is a decay constant of the quartz resonator. It can be seen as the ratio between the energy lost per oscillation and the product of a constant per the total energy stored in the system (in other words, the ratio between dissipated energy and conserved energy) (Dixon, 2008): D¼

Edissipated 2π  Estored

While Δf correlates with adsorbed/desorbed amount of sample, and D correlates with the sample’s rigidity and viscoelasticity. QCM is extremely versatile and can assess interactions with different materials (Fee, 2013). Initially, QCM was used mainly under vacuum, as a gas sensor; later, its use was extended to liquid environments, proving to be more effective to detect biomolecules of medical interest. QCM can be thought and modeled according to the Butterworth–vanDyke (BVD) equivalent circuit (Arnau, Jimenez, & Sogorb, 2001). BVD is made up of two arms: a motional or acoustic arm, which has three series components modified by the mass and viscous loading of the crystal (a resistor, a capacitor, and an inductor), shunted by the second arm, or electrical parasitic capacitance (that is to say, the sum of the static capacitances of the crystal’s electrodes, holder, and connector capacitance). In the BVD model, the motional inductance is proportional to the mass, the motional capacitance is inversely proportional to the stiffness, and the motional resistance enables to quantify dissipative losses. For further details concerning the physics underlying the QCM, the reader is referred to Dixon (2008). QCM is a label-free biosensor (Cooper, 2003), and therefore, offers several advantages with respect to labeled biosensors (Sin, Mach,

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Wong, & Liao, 2014). QCM sensors can be of many types (Marx, 2003): DNA sensors, enzyme sensors, immunosensors (Aberl, K€ oßlinger, & Wolf, 1998; K€ oßlinger et al., 1994), with sensor surfaces functionalized and coated with antibodies, cell/tissue/microorganism-based sensors, or aptasensors. As far as immunosensors are concerned, antibodies can be immobilized using cross-linking compounds, entrapping matrices (usually complex polymeric mixtures) (Kurosawa et al., 2006; Zeng, Shen, & Mernaugh, 2012). Aptamers are an emerging class of synthetic molecules, artificially engineered in such a way that they can act as a universal molecular recognition component, interacting with a great variety of molecules. They can be designed using the systematic evolution of ligands by exponential enrichment (SELEX) technology (Seok Kim, Ahmad Raston, & Bock Gu, 2015; Sun & Zu, 2015). Because of their unique properties, aptamers are employed for many clinical and industrial applications, being even superior to antibodies in fields like biomarker discovery, diagnostics, controlled drug delivery and release, personalized treatment, and safety/hygiene (Sharma, Ragavan, Thakur, & Raghavarao, 2015; Sun & Zu, 2015). Further, the sensing platform can be conjugated with nanoparticles, carbon nanotubes, or magnetic beads in order to increase sensitivity and to further amplify the signal (Holzinger, Le Goff, & Cosnier, 2014; Jianrong, Yuqing, Nongyue, Xiaohua, & Sijiao, 2004; Wang, 2005). QCM can be also coupled with electrochemical devices: the so-called electrochemical QCM (EQCM), which enables the study of changes in mass and viscoelasticity/dissipation, relating these parameters with electron kinetics (Buttry & Ward, 1992; Deakin & Buttry, 1989; Grieshaber, MacKenzie, V€ or€ os, & Reimhult, 2008; Marx, 2007).

4. BACTERIA AND QCM: A NEW STRATEGY FOR DETECTING MICROBIAL POPULATION QCM allows to study bacteria biofilms. Tam and collaborators implemented a DNA QCM sensor for detecting Streptococcus mutans (S. mutans), implicated in dental caries (Kreth et al., 2004; Tam, Ayala, Kinsinger, & Myung, 2005; Tam et al., 2007). Schofield and coauthors developed a QCM-D for online, in situ monitoring of growth of S. mutans (Schofield, Rudd, Martin, Fernig, & Edwards, 2007). QCM

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allows also to study the growth and adhesion mechanisms of Streptococcus salivarius (Olsson, Arun, et al., 2012; Olsson, Sharma, Mei, & Busscher, 2012; Olsson, van der Mei, Busscher, & Sharma, 2009, 2010), Streptococcus gordonii (Eichler et al., 2011; Krajewski et al., 2014), among others.

5. QCM AND NOSOCOMIAL INFECTIONS Nosocomial infections are usually defined as infections occurring within 2 days of hospital admission, 3 days of discharge, or 30 days of a surgical operation and affect 1 out of 10 patients. Nosocomial infections are responsible of 5000 deaths per year and therefore carry a heavy socioeconomic burden (Inweregbu, Dave, & Pittard, 2005). Marcus and collaborators fabricated a QCM-D for studying the biofilm formation of Pseudomonas aeruginosa (P. aeruginosa) (Marcus, Herzberg, Walker, & Freger, 2012). The group of Kim developed a continuous QCM immunosensor, which could detect concentrations of the P. aeruginosa cells in the range 1.3  107 to 1.3  108 CFU/mL (Kim, Park, & Kim, 2004). Pang and coworkers detected the pathogen using a wireless magnetoelastic sensing device in the concentration range of 103– 108 cells/mL (Pang et al., 2007). QCM can be used to study also other members of the Pseudomonadaceae family, like Pseudomonas putida and Pseudomonas fluorescens (Chen et al., 2010; Sprung et al., 2009; Sun, Lu, Boluk, & Liu, 2014). Staphylococcus epidermidis is another nosocomial pathogen. Xia and coworkers fabricated a QCM genosensor conjugated with gold nanoparticles for the detection of this bacterium in real clinical samples, with a limit of detection of 1.3  103 CFU/mL. The sensitivity and specificity of the device were 97.14% and 100%, respectively (Xia, Zhang, & Jiang, 2011). Finally, the group of Shih developed a QCM-based electronic nose for monitoring in real time the exhaled breath of ventilator-assisted patients in an intensive care unit, which is the hospital setting with the highest prevalence of hospital-acquired infections. The device was characterized by a 98% accuracy and enabled the detection of different pathogens, such as P. aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae, Staphylococcus aureus (S. aureus), and Acinetobacter lwoffii (Shih, Lin, Lee, Chien, & Drake, 2010).

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6. BACTERIA AND QCM: ROLE IN MONITORING ANTIMICROBIAL SUSCEPTIBILITY AND DRUG RESISTANCE This paragraph makes an overview of the potential of QCM in monitoring antimicrobial susceptibility and drugs resistance, thus enabling infection control. QCM-D was exploited by Guntupalli and collaborators in order to detect drug-resistant S. aureus, which represents a serious public health concern (Spagnolo et al., 2014). The authors coupled different genetic engineering and advanced nanotechnologies, including the Langmuir–Blodgett (LB) technique, which enables to produce highly structured and compact layers. They successfully implemented a QCM immunosensor conjugated with latex beads and coated with antibodies targeting the penicillin-binding protein (PBP 2a) and with a thin film of a transformed lytic bacteriophage converted into phage spheroids. The device proved to be able to discriminate between methicillin-resistant S. aureus strains and methicillin-sensitive S. aureus strains (MSSA) (Guntupalli et al., 2013). QCM-D appears to be promising for monitoring drug resistance, as well as screening new antibacterial or antiviral drug candidates. Joshi and colleagues used QCM-D to shed light on the mechanism of aminoglycosides, kanamycin A, and neomycin B, toward bacterial membranes ( Joshi, Voo, Graham, Spiccia, & Martin, 2015). Ma and colleagues designed a label-free lectin-based biosensor for assessing the binding between the Concanavalin A (Con A), immobilized through a polythiophene interface containing fused quinone moieties glycosylated, and the lipopolysaccharide on Gramnegative bacteria. They developed an improved version of orthogonal EQCM for characterizing the antimicrobial activities of ciprofloxacin, ceftriaxone, and tetracycline against Escherichia coli W1485 (Ma, Rehman, Sims, & Zeng, 2015). Another biotinylated Con A-modified EQCM biosensor proved to be effective in detecting E. coli, with a detection limit of approximately 1.0  103 to 1.0  104 CFU/mL (Serra, Gamella, Reviejo, & Pingarro´n, 2008). Carbohydrate- and lectin-based QCM biosensors appear indeed promising in detecting and monitoring microbes, for example, E. coli in the range 7.5  102 to 7.5  107 cells/mL (Shen, Yan, Parl, Mernaugh, & Zeng, 2007). Wang and collaborators characterized the antimicrobial properties of some antimicrobial peptides (AMPs), namely alamethicin, chrysophsin-3,

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indolicidin, and sheep myeloid antimicrobial peptide, when interacting with bacterial inner membrane and disrupting its lipid bilayer structure (Wang, Nagarajan, & Camesano, 2014, 2015; Wang, Nagarajan, Mello, & Camesano, 2011; Wang, Wu, Kucˇa, Dohnal, & Tian, 2014). Ivanov and coauthors explored the antimicrobial activity of another AMP, chrysophsin-1. McCubbin and colleagues exploited QCM-D to investigate other AMPs, such as caerin 1.1 wild type, two caerin 1.1 mutants (Gly15Gly19-caerin 1.1 and Ala15Ala19-caerin 1.1), aurein 1.2, and oncocin. Oncocin appears to be a strong against Gram-negative human pathogens (Knappe et al., 2010). Weckman and collaborators used QCM to screen the potential antimicrobial activities of cranberry-derived proanthocyanidins, testing it against P. aeruginosa and E. coli O111:B4 (Weckman, Olsson, & Tufenkji, 2014). Forbes and coauthors studied the antibacterial activity and antibiofilm efficacy against S. aureus of a novel peptide derived from human apolipoprotein E (apoEdpL-W) (Forbes et al., 2013). Hyldgaard and collaborators investigated monocaprylate’s potential antimicrobial effect against E. coli, Staphylococcus xylosus, and Zygosaccharomyces bailii (Hyldgaard, Sutherland, Sundh, Mygind, & Meyer, 2012). Cecropin P1 revealed anti E. coli properties, in a study performed by Strauss and collaborators (Strauss, Kadilak, Cronin, Mello, & Camesano, 2010). Ding and coworkers studied the antimicrobial activity against S. aureus of a series of diblock copolymers of PEG-b-cationic polycarbonates coated onto silicone rubber for the prevention of intravascular catheter-associated infections (Ding et al., 2012). Yoshinari and collaborators investigated the antimicrobial activity of titanium surfaces coated with histatin 5 and lactoferricin, in order to prevent Porphyromonas gingivalis-induced peri-implantitis (Yoshinari, Kato, Matsuzaka, Hayakawa, & Shiba, 2010). The group of Khoo investigated the antimicrobial properties of titanium surfaces coated with PEGylatedpeptide with HKH tripeptide motif, active against S. aureus (Khoo et al., 2009). Sherman and coworkers coupled QCM with nuclear magnetic resonance to assess the biocidal properties of peptides, such as fallaxidin 4.1a, structurally a C-terminal amidated analogue of fallaxidin 4.1, a cationic peptide isolated from the amphibian Litoria fallax (Sherman et al., 2009). Christ and colleagues used QCM to study the activity of lantibiotics, a group of lanthionine-containing peptides, and in particular of nisin and gallidermin (Christ et al., 2008).

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7. INFLUENZA VIRUS AND OTHER RESPIRATORY VIRUSES Influenza virus is a negative-sense single-stranded RNA virus (Gasparini, Amicizia, Lai, Bragazzi, & Panatto, 2014). Influenza is an acute respiratory disease, which has an enormous socio-economic burden in term of costs due to lost productivity in adults and to hospitalizations and treatments in high-risk subjects (Gasparini, Amicizia, Lai, & Panatto, 2012a, 2012b; Molinari et al., 2007). The World Health Organization (WHO) estimates that seasonal influenza causes up to 500,000 deaths worldwide each year, the mortality being higher in pandemic periods (Lai et al., 2011). Among the adult population in Italy, a cost of €940.39 per case has been estimated (Gasparini et al., 2012a, 2012b). Current diagnostic techniques include Madin–Darby Canine Kidney cell culture, complement fixation, hemagglutinin-inhibition, and PCR/ RT-PCR. These approaches are highly sensitive and specific but present the drawbacks of being time-consuming and of requiring specialized laboratories and trained personnel (Amano & Cheng, 2005). QCM immunosensors, such as sensors coated with antihemagglutinin (HA) or antinucleoprotein antibodies, QCM genosensors or QCM aptasensors appear promising at the point-of-care level (Krejcova et al., 2014). The use of QCM as biosensor for detecting and quantifying influenza virus was pioneered by the group of Sato in the 1990s. The authors realized a QCM sensor coated with monosialoganglioside (GM3) monolayers reconstituted in glucosylceramide and sphingomyelin membranes (Sato, Serizawa, & Okahata, 1996). Le Brun and coauthors developed a QCM immunosensor coated with a self-assembling monolayer (SAM) of synthetic peptides and IgG antinucleoprotein. Kim and collaborators used a ProLinker™ B, a calixcrown derivative, coated QCM to detect H3N2 canine influenza virus in saliva samples, with a 97.1% sensitivity and 94.7% specificity. Diltemiz and coworkers implemented a 4-aminophenyl boronic acidmodified QCM for the detection of HA with a detection limit of 4.7  102 μM (0.26 μg/mL) (Diltemiz, Ers€ oz, Hu¨r, Kec¸ili, & Say, 2013). Brockman and colleagues designed a QCM-based aptasensor conjugated with magnetic nanobeads for the detection of avian influenza virus (AIV) H5N1. The detection limit of the aptasensor was 1 HAU (HA unit)

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(Brockman, Wang, Lum, & Li, 2013). Wang and Li developed a QCM aptasensor based on single-stranded DNA (ssDNA) cross-linked polymeric hydrogel for detection of AIV H5N1, using SAM approach. The detection limit of the assay was of 0.0128 HAU (Wang & Li, 2013). The group of Peduru Hewa developed a QCM-based immunosensor conjugated with gold nanoparticles for both influenza A and B viruses in clinical samples (namely, nasal washes) with the detection limit of 1.0  103 to 1.0  104 PFU/mL (Peduru Hewa, Tannock, Mainwaring, Harrison, & Fecondo, 2009). Li and collaborators implemented a QCM coated with SAMs of anti-HA antibodies immobilized through 16-mercaptohexadecanoic acid and conjugated with magnetic nanobeads for the detection of AIV H5N1 virus detection (Li et al., 2011). Miller and coworkers designed a QCM immunosensor conjugated with modified gold nanoparticles coated with epitopes associated with HA and FLAG peptides and a six-mer PEG spacer, for the detection of H5N1 virus (Miller, Hiatt, Keil, Wright, & Cliffel, 2011). Wangchareansak and collaborators implemented a QCM biosensor coated with N-acetylglucosamine (GlcNAc) linked to p-nitrophenol, for the detection of different strains of influenza A virus (namely, H5N3, H5N1, and H1N3) (Wangchareansak, Sangma, Ngernmeesri, Thitithanyanont, & Lieberzeit, 2013; Wangchareansak et al., 2013). The group of Takeda fabricated carbon nanotube sensors and QCM immunosensor, both coated with anti-HA antibodies (Takeda et al., 2007). Owen and coworkers used QCM immunosensor for the direct detection of aerosolized influenza A virions with a limit of detection of 4 virus particles/mL (Owen, Al-Kaysi, Bardeen, & Cheng, 2007) (Table 1). Further, QCM is used also to shed light on the mechanism of influenza virus (Gerdon et al., 2005). For example, Takahashi and coworkers studied the association of a sulfated galactosyl ceramide (sulfatide) with the viral envelope glycoprotein HA. Tanaka and collaborators studied the dynamics between glycopolymers bearing sialyloligosaccharide and influenza virus via QCM (Tanaka et al., 2014). Early stage detection is essential for effective treatment of pediatric virus infections. QCM hold promises also for the detection of respiratory syncytial viruses, which carry a heavy clinical and socio-economic burden in children and their households (Esposito et al., 2005). Lee and collaborators developed a QCM for the detection of vaccine viruses, showing the feasibility of using QCM for a rapid detection of airborne pathogens (Lee, Jang, Akin, Savran, & Bashir, 2008).

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Table 1 Microorganisms Detected with QCM-Based Sensors Detected Microorganism References

Acinetobacter baumannii

Shih et al. (2010)

Acinetobacter lwoffii

Shih et al. (2010)

African swine fever virus

Uttenthaler, K€ osslinger, and Drost (1998)

Bacillus anthracis

Gerdon, Wright, and Cliffel (2006), Ghosh, Ostanin, Johnson, Lowe, and Seshia (2011), Hao et al. (2009, 2011), Lee, Song, Hwang, and Lee (2013), Oztuna and Nazir (2012), Oztuna, Nazir, and Baysallar (2014), Petrenko (2008), Sanvicens, Pastells, Pascual, and Marco (2009), Skottrup, Nicolaisen, and Justesen (2008), and Wan et al. (2006)

Bacillus atrophaeus

Alava et al. (2009) and Farka, Kova´rˇ, Pribyl, and Skla´dal (2013)

Bacillus cereus

Olofsson, Hermansson, and Elwing (2005), Skottrup et al. (2008), Susmel, Toniolo, Pizzariello, Dossi, and Bontempelli (2005), Vaughan, Carter, O’Sullivan, and Guilbault (2003), and Wirtanen et al. (2002)

Bacillus subtilis

Ghosh et al. (2011), Jenkins et al. (2004), Lee (2005), and Poitras and Tufenkji (2009)

Bordetella pertussis

Janshoff et al. (1997) and Shur, Wu, Cropek, and Banta (2011)

Bacillus thuringiensis

Thammasittirong, Dechklar, Leetachewa, Pootanakit, and Angsuthanasombat (2011)

Burkholderia cepacia

Marradi, Martı´n-Lomas, and Penade´s (2010)

Campylobacter jejuni

Safina, van Lier, and Danielsson (2008), Skottrup om, et al. (2008), Yakovleva, Moran, Safina, Wadstr€ and Danielsson (2011), and Yang, Kirsch, and Simonian (2013)

Cattle bovine ephemeral fever virus

Lee and Chang (2005)

Chlamydia trachomatis

Ben-Dov, Willner, and Zisman (1997)

Clostridium perfringens

Cai et al. (2011)

Clostridium tetani

Cai et al. (2011)

Cryptosporidium parvum

Bridle et al. (2012) and Poitras, Fatisson, and Tufenkji (2009)

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Table 1 Microorganisms Detected with QCM-Based Sensors—cont'd Detected Microorganism References

Dengue virus

Chen et al. (2009), Peh, Leo, and Toh (2011), Su, Wu, Chen, Yang, and Tai (2003), Tai, Lin, Wu, Huang, and Shu (2006), Teles (2011), and Wu et al. (2005)

Ebola virus

Yu et al. (2006)

Edwardsiella tarda

Choi and Choi (2012)

Enterobacter spp.

Uzawa et al. (2002)

Escherichia coli

Cai et al. (2011), Guo, Lin, Chen, Ye, and Wu (2012), Gutman, Walker, Freger, and Herzberg (2013), Han, Chang, Hsu, and Chen (2009), Inomata, Tanabashi, Funahashi, Ozawa, and Masuda (2013), Jiang et al. (2011), Kim and Park (2003), Latif, Qian, Can, and Dickert (2014), Mao, Yang, Su, and Li (2006), Poitras and Tufenkji (2009), Su and Li (2004a, 2004b, 2005a, 2005b), Tijing, Jung, Kim, and Kim (2011), and Wu, Chen, and Lin (2007)

Francisella tularensis

Kleo et al. (2012), Pohanka (2009), Pohanka and Skladal (2005, 2007)

Fusarium spp.

Eifler et al. (2011)

General purpose (a wide range of pathogens)

Welch et al. (2014)

Helicobacter pylori

Parreira et al. (2013) and Safina et al. (2008)

Hepatitis B virus

Yao and Fu (2014), Yao et al. (2008), and Zhou, Liu, Hu, Wang, and Hu (2002)

Herpes simplex (HSV1)

Cooper et al. (2001), Shinde, Fernandes, and Patravale (2012), and Uludag˘, Hammond, and Cooper (2010)

HIV

Alfonta et al. (2004), Boyle, Hawkins, Steele, Singhal, and Cheng (2012), Lee et al. (2010), Lu et al. (2012), Mohan and Prakash (2010), Tombelli, Minunni, Luzi, and Mascini (2005), Wen, Shan, Pu, & Liu (2009), and Yang (2005)

HPV

Mobley et al. (2014) and Prakrankamanant et al. (2013) Continued

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Table 1 Microorganisms Detected with QCM-Based Sensors—cont'd Detected Microorganism References

Influenza virus

Amano and Cheng (2005), Brockman et al. (2013), Diltemiz et al. (2013), Gerdon, Wright, and Cliffel (2005), Krejcova et al. (2014), Li et al. (2011), Miller et al. (2011), Owen et al. (2007), Peduru Hewa et al. (2009), Sato et al. (1996), Takeda et al. (2007), Tanaka et al. (2014), Wang and Li (2013), Wangchareansak et al. (2013), and Wangchareansak et al. (2013)

Klebsiella pneumoniae

Shih et al. (2010)

Legionella pneumophila

Decker et al. (2000)

Leishmania

Ramos-Jesus et al. (2011) and Souto, Faria, de Andrade, and Kubota (2015)

Listeria monocytogenes

Vaughan, O’Sullivan, and Guilbault (2001)

M13 phage

Uttenthaler, Schra¨mL, Mandel, and Drost (2001)

MS2 bacteriophage

Lu, Mosiman, and Nguyen (2013)

Mycobacterium tuberculosis

Hiatt and Cliffel (2012) and Serra et al. (2008)

Neisseria meningitidis

Otto (2008), Marradi et al. (2010), and Reddy, Mainwaring, Kobaisi, Zeephongsekul, and Fecondo (2012)

Norovirus

Imai et al. (2011) and Rydell, Dahlin, H€ oo €k, and Larson (2009)

Orchid virus

Eun, Huang, Chew, Li, and Wong (2002a, 2002b)

Parvovirus

Dorsch et al. (2001) and Kim et al. (2015)

Plasmodium falciparum

Ittarat et al. (2013), Joergensen et al. (2010), Lee et al. (2012), Potipitak, NgrenngarmLert, Promptmas, Chomean, and Ittarat (2011), and Wangmaung et al. (2014)

Plasmodium vivax

Ittarat et al. (2013), Lee et al. (2012), and Wangmaung et al. (2014)

Poliovirus

Imai et al. (2011)

Pox virus

Kulkarni, Kellaway, and Kotwal (2012)

Pseudomonas aeruginosa

Cai et al. (2011), Kim et al. (2004), Marcus et al. (2012), Pang et al. (2007), and Shih et al. (2010)

Pseudomonas fluorescens

Chen et al. (2010) and Sun et al. (2014)

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Table 1 Microorganisms Detected with QCM-Based Sensors—cont'd Detected Microorganism References

Pseudomonas putida

Sprung et al. (2009)

Respiratory syncytial Virus

Perez, Adams, Zimmerman, Haselton, and Wright (2013)

Rotavirus

Imai et al. (2011)

Salmonella enteritidis

Si, Li, Fung, and Zhu (2001)

Salmonella paratyphi

Fung and Wong (2001) and Kengne-Momo et al. (2012)

Salmonella typhimurium

Babacan, Pivarnik, Letcher, and Rand (2002), Kim, Rand, and Letcher (2003), Olsen et al. (2003), Ozalp, Bayramoglu, Erdem, and Arica (2015), Salam, Uludag, and Tothill (2013), Sankaran, Panigrahi, and Mallik (2011), Su and Li (2004a, 2004b, 2005a, 2005b), and Zhu, Shih, and Shih (2007)

Shigella dysenteriae Staphylococcus aureus

Guntupalli et al. (2013), Miyao, Ikeda, Shiraishi, Kawakami, and Sueda (2015), Olsson, Arun, et al. (2012), Olsson, Sharma, et al. (2012), and Shih et al. (2010)

Staphylococcus epidermidis

Xia et al. (2011)

Streptococcus gordonii

Eichler et al. (2011) and Krajewski et al. (2014)

Streptococcus mutans

Kreth et al. (2004), Otto (2008), Schofield et al. (2007), Skottrup et al. (2008), and Tam et al. (2005, 2007)

Streptococcus pneumoniae

Cai et al. (2011)

Streptococcus salivarius

Olsson et al. (2009, 2010), Olsson, Arun, et al. (2012), and Olsson, Sharma, et al. (2012)

Vibrio cholerae

Alfonta, Willner, Throckmorton, and Singh (2001), Carter, Mekalanos, Jacobs, Lubrano, and Guilbault (1995), Fraser et al. (2012), Janshoff et al. (1997), Matsubara, Ishikawa, Taki, Okahata, and Sato (1999), and Stine, Pishko, and Schengrund (2004)

Vibrio harveyi

Buchatip et al. (2010)

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8. SEXUALLY TRANSMITTED DISEASES Sexually transmitted diseases (STDs) carry a relevant epidemiological burden. Further, antibiotic-resistant pathogens causing STDs are increasingly and cause a serious public health problem (Katz, Lee, & Wasserman, 2012). Therefore, biosensors can be helpful in diagnosing such microorganisms (Shinde et al., 2012).

8.1 Viral Hepatitis Hepatitis B virus (HBV) is an enveloped, not-fully double-stranded DNA virus. It is a member of the genus Orthohepadnavirus and of the family Hepadnaviridae and has been classified into four major serotypes (adr, adw, ayr, and ayw) based on antigenic epitopes present on its envelope proteins (E-proteins) and into 10 genotypes (the well-known A–H and the recently identified I and J genotypes, described in Vietnam and in Japan) (Tatematsu et al., 2009; Tran, Trinh, & Abe, 2008), which can be further subdivided into over 40 subgenotypes, according to the nucleotide sequence of the genome (Locarnini, Hatzakis, Chen, & Lok, 2015; Thedja et al., 2015). It is distributed worldwide and is endemic in some populations (Croagh, Desmond, & Bell, 2015; Locarnini et al., 2015). The virus can be transmitted with the blood or other body fluids; common routes of transmission include vertical transmission, sexual intercourse, blood transfusions, injecting drug use, and occupational exposure among health-care workers (Locarnini et al., 2015). HBV infection from acute can become chronic and evolving can lead to chronic hepatitis, hepatocirrhosis, and liver cancer. Patient’s diet, other comorbidities can influence the natural history of HBV. The genetic makeup is important too: some particular HBV genotypes, like basal core promoter A1762T/G1764A mutant, and precore G1896A mutant, or host’s polymorphisms may increase the risk, as found by the REVEAL-HBV (risk evaluation of viral load elevation and associated liver disease/cancer-HBV) group. It is estimated that there are at least 2 billion people with a past or current HBV infection, and more than 240 million patients suffering from chronic HBV-associated liver disease, worldwide (WHO, 2015). Gold standards for the diagnosis of HBV include immunological methods, such as enzyme immunoassay, radioimmunoassay, immunochromatographic assay, and immunochemiluminescence, as well as more sophisticated molecular approaches like nonamplification- and amplification-based methods (Heiat,

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Ranjbar, & Alavian, 2014). Biosensors appear promising in detecting HBV more quickly and effectively, stratifying patient’s according to the viral load. Xu and coauthors designed a diaphragm-based QCM immunosensor for simultaneous detection of HBsAg-antibodies, with a detection limit of 0.1 ng/mL and a dynamic detection range of 0.1–103 ng/mL (Xu et al., 2014). Yao and collaborators used peptide nucleic acid (PNA) probes to construct modified PNA-QCM biosensors for real-time monitoring of the hybridization assay of HBV. This assay was characterized by a detection limit of 8.6 pg/L and by a clinical specificity of 94.44% when compared with conventional RT-PCR. The device could be further improved by adding RecA-protein-coated complementary ssDNA (Yao et al., 2008) (Table 2). Table 2 An Overview of the Applications of QCM in the Field of Public Health Applications Examples

Biohazard and bioterrorism

Detection of pathogens, such as Bacillus anthracis and Francisella tularensis

Chronic degenerative Detection of biomarkers of chronic degenerative diseases, diseases such as atherosclerosis, diabetes, retinal, and macular degeneration Environmental monitoring

Vapor sensing and monitoring

Food safety assurance Detection of food-borne pathogens Homeland security

Detection of explosive gases and chemical warfares

Infection control

Detection of HBV Detection of influenza virus Detection of pathogens causing invasive diseases Detection of pathogens causing sexually transmitted diseases

Occupational hygiene

Risk assessment at workplace

Oncology

Carcinogenesis and mutagenesis testing Early cancer detection

Pharmaceutical discovery

Discovery of alamethicin, chrysophsin-1, chrysophsin-3, indolicidin, and sheep myeloid antimicrobial peptide as new emerging compounds for addressing the issue of drug resistance

Water safety assurance

Bioremediation Detection of water-borne pathogens

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Rolling circle amplification (RCA), which is an isothermal amplification technique for small circular DNA templates, was exploited to develop a RCA-based QCM biosensors for direct detection of HBV genomic sequence from clinical samples. The detection limit was of 104 copies/mL HBV DNA (Yao, Gao, & Cheng, 2009) (Table 3). Zhou and collaborators designed a QCM genosensor coated with a detection limit of 0.02–0.14 μg/mL (Zhou et al., 2002). Shen and collaborators developed a functionalized QCM immunosensor coated with antibodies targeting HBV surface antigen and with hyperbranched polymer, previously synthesized from p-phenylenediamine and trimesic acid, cysteamine monolayers, and protein A. The device was characterized by a detection limit of 0.53 μg/mL (Shen, Cai, Wang, & Lu, 2011; Shen, Wang, et al., 2011). Sakashita and collaborators exploited QCM for investigating potential therapies against HBV-related diseases, in particular for developing new highly efficient drug delivering strategies. The interaction between galactose- or N-acetylgalactosamine-modified lipid plus plasmid DNA (lipoplex) and asialoglycoprotein receptor (ASGPR) was investigated, with and without the treatment with asialofetuin.

8.2 Chlamydia Chlamydia trachomatis (C. trachomatis), an obligate intracellular human pathogen and a Gram-negative bacterium, includes three human biovars: serovars Ab, B, Ba, or C, which cause trachoma; D–K, which cause urethritis, pelvic inflammatory disease, ectopic pregnancy, and neonatal pneumonia and conjunctivitis; and L1, L2, and L3, which cause lymphogranuloma venereum (Ceovic & Gulin, 2015). The group of Ben-Dov developed a QCMbased immunosensor for the detection of C. trachomatis in urine samples with a detection limit in the concentration range 260 ng/mL to 7.8 μg/mL (BenDov et al., 1997).

8.3 HIV/AIDS The human immunodeficiency virus (HIV) is a retrovirus and specifically a lentivirus that causes HIV infection and acquired immunodeficiency syndrome (AIDS). Biosensors can provide a quick and effective CD4+ T-cells at the point-of-care level, especially in developing countries, determining patients eligibility for antiretroviral therapy as well as monitoring its efficacy (Boyle et al., 2012; Glynn, Kinahan, & Ducre´e, 2013).

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Table 3 An Overview of Chemical Compounds Detected with QCM-Based Sensors Detected Chemical Compound References

Acetic acid VOCs

Panigrahi, Sankaran, Mallik, Gaddam, and Hanson (2012)

Aldicarb

Liu, Nordin, Li, and Voiculescu (2014)

Ametrex

Pogorelova, Bourenko, Kharitonov, and Willner (2002)

Atrazine

Hromadova´ et al. (2013), Jia, Toury, and Ionescu (2012), and Pogorelova et al. (2002)

Benzene

Finklea, Phillippi, Lompert, and Grate (1998), Gapan, Evyapan, NamLi, Turhan, and Stanciu (2005), Hou, Rehman, and Zeng (2011), and Ogawa and Sugimoto (2002)

Bisphenol A

Li, Morita, Ye, Tanaka, and Osawa (2004), Murata, Yano, Fukuma, Maeda, and Katayama (2004), Ragavan, Rastogi, and Thakur (2013), and Rahman, Shiddiky, Park, and Shim (2007)

Carbaryl

Karousos, Aouabdi, Way, and Reddy (2002), March et al. (2015), and Yao et al. (2009)

Chlorpyrifos

Jiang et al. (2014) and March, Manclu´s, Jime´nez, Arnau, and Montoya (2009)

Chromium

Carrington, Yong, and Xue (2006) and Murray and Deshaires (2000)

Copper

Jin, Huang, Liu, and Zhao (2013) and Yamasaki, Cunha, Oliveira, Duarte, and Gomes (2004)

Cyanide ions

Timofeyenko, Rosentreter, and Mayo (2007)

Daminozide

Yan, Fang, and Gao (2007)

Dibutyl phthalate

Wang et al. (2013)

Dichloromethane

Hou et al. (2011)

Dichlorvos

Karousos et al. (2002)

Dioxin

Farre´, Pe´rez, Gonc¸alves, Alpendurada, and Barcelo´ (2010), Kurosawa, Aizawa, and Park (2005), Kurosawa et al. (2006), Mascini et al. (2004), Park et al. (2003, 2006), and Zhou and Cao (2001)

Haloacetic acids and related contaminants

Suedee, Intakong, and Dickert (2006) Continued

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Table 3 An Overview of Chemical Compounds Detected with QCM-Based Sensors— cont'd Detected Chemical Compound References

Hexachlorobenzene

Das, Penelle, and Rotello (2003) and Wu, Lu, Jin, Zhang, and Chen (2011)

Histamine

Dai, Zhang, Pan, Kong, and Wang (2014)

Imidacloprid

Bi and Yang (2009)

Kerosene

Ogawa and Sugimoto (2002) and Ueyama, Hijikata, and Hirotsuji (2002)

Lead

Switzer, Rajasekharan, Boonsalee, Kulp, and Bohannan (2006)

Methane

Hou et al. (2011)

Nicotine

Huynh et al. (2015), Liu et al. (2014), and Noworyta, Kutner, Wijesinghe, Srour, and D’Souza (2012)

Polyisobutylene

Pejcic et al. (2012)

Prometre

Pogorelova et al. (2002)

Prozine

Pogorelova et al. (2002)

Simane

Pogorelova et al. (2002)

Terbute

Pogorelova et al. (2002)

Thiabendazole

March et al. (2015)

Thiacloprid

Bi and Yang (2009)

Toluene

Finklea et al. (1998), Ishii, Naganawa, Nishioka, and Hanaoka (2013), Shinar, Liu, and Porter (2000), and Tang et al. (2014)

Trichloroethylene

Finklea et al. (1998)

Triclopyr

March et al. (2009)

Tyllane

Pogorelova et al. (2002)

In the specific field of QCM biosensors, Lee and coworkers implemented a QCM-D for detecting the conformational rearrangement of glycoprotein gp120 when engaged with different ligands (Lee et al., 2010). Lu and coworkers developed a QCM aptasensor coated with a synthetic 35-aminoacides peptide similar to residues 579–613 of gp41, based on

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epitope imprinting technique, with a detection limit of 2 ng/mL (Lu et al., 2012). Tombelli and coauthors constructed a RNA aptamer-based QCM for detecting transactivator of transcription (Tat) protein (Tombelli et al., 2005). Wen and colleagues fabricated a QCM sensor with sensitivity 91.7%, specificity 93.3%, and accuracy (Wen et al., 2009). Mohan and Prakash fabricated a QCM coated with polyanthranilic acid as azidothymidine drug sensor with a limit detection of 1 μM (Mohan & Prakash, 2010). The group of Alfonta exploited QCM immunosensor technology for detecting mutated, drug-resistant HIV-1, and its response to a protease inhibitor, namely saquinavir, in infected cultured cells and in blood samples of AIDS patients (Alfonta et al., 2004). QCM, therefore, enables the discovery of new drugs and allows to overcome the emergence of drugresistant strains (Yang, 2005).

8.4 Herpes Simplex Virus Herpes simplex virus (HSV) 1 and 2 belong to the herpesvirus family, Herpesviridae. They are widely distributed and highly contagious, being one of the commonest STDs, HSV1 causes cold sore and HSV2 genital herpes. The group of Cooper developed a QCM immunosensor for detection of HSV1, using an innovative technique termed as rupture event scanning, with the sensitivity down to a single virus particle. Uludag˘ and collaborators developed a QCM genosensor conjugated with gold nanoparticles with a limit of detection sensitivity of 5.2  1012 M (Uludag˘ et al., 2010).

9. INVASIVE DISEASES Invasive bacterial infections occur when the bacteria get past the defenses of infected subjects, usually frail, with chronic illnesses or with serious impairment of the immune system. Neisseria meningitidis, a Gram-negative β-proteobacterium, causes bacterial meningitis (Gasparini et al., 2012a, 2012b). The incidence of this severe disease varies from very few cases to more than 1000 cases per 100,000 inhabitants per year (Gianchecchi, Torelli, Piccini, Piccirella, & Montomoli, 2015). Classical gold standard diagnostic techniques are time-consuming and given the importance of an early diagnosis, there is an urgent need for a rapid, sensitive, and specific diagnostic tool (Marradi et al., 2010; Otto, 2008). Reddy and coworkers implemented a QCM immunosensor coated with antibodies against the cell surface outer membrane protein 85, previously

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immobilized with polyvinylidene fluoride and protein A, and conjugated with nanoparticles. The assay had a sensitivity of 300 ng/mL (Reddy et al., 2012). Streptococcus pneumoniae is a Gram-positive, facultative anaerobic bacterium, residing asymptomatically in the nasopharynx of healthy carriers. It is the main cause of community-acquired pneumonia and meningitis in children, the elderly, and immunocompromised subjects. There is only one QCM sensor, which has already been described (Cai et al., 2011).

10. TROPICAL AND TROPICAL NEGLECTED DISEASES Tropical neglected diseases include Ebola and Dengue. QCM-based biosensors, being scalable and affordable, can be particularly helpful for the diagnosis and monitoring of these infections. Ebola virus is the cause of the 2013–2015 Ebola virus epidemic outbreak in West Africa, which has resulted in at least 27,678 suspected cases and 11,276 confirmed deaths (WHO, 2015). Yu and coauthors developed a QCM-modified immunosensor for the detection of different human Ebola virus species (including Zaire, Sudan, and Ivory Coast), coating the electrode surface with polyclonal and monoclonal antibodies (MAbs) against Ebola virus envelope glycoprotein (Yu et al., 2006). Dengue fever, also termed as breakbone fever, is a mosquito-borne disease, which can evolve in a small fraction of cases into the deadly dengue hemorrhagic fever or into the dengue shock syndrome. Rapid diagnosis of dengue is therefore crucial (Peh et al., 2011; Teles, 2011; Zhang, Bai, Pi, Yang, & Cai, 2015). Chen and coauthors designed a DNA QCM sensor conjugated with gold nanoparticles. The detection limit was of 2 PFU/mL (Chen et al., 2009). Tai and colleagues used a QCM coated with molecularly imprinted films of the linear pentadecapeptide epitope of the dengue virus NS-1 protein (Tai et al., 2006). The group of Su fabricated a QCM immunosensor coated with antibodies against the dengue virus E-protein and nonstructural 1 protein (NS-1 protein) (Su et al., 2003). Wu and colleagues fabricated a QCM immunosensor with a detection limit of 1.727 μg/mL (for E-protein) and of 0.740 μg/mL (for NS-1 protein) (Wu et al., 2005). Malaria is another important health problem in the tropical and subtropical countries. The most severe form of malaria is caused by Plasmodium falciparum (P. falciparum). The standard diagnostic method is Giemsa-stained peripheral blood smear to visualize parasite morphology, which is however

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ineffective in cases of low parasitemia or mixed infection. Molecular techniques can overcome this issue. The group of Potipitak fabricated a QCM genosensor immobilizing merozoite surface protein 2 (msp2) gene. The sensor proved to sensitive at the subnanogram level, specific for P. falciparum (Potipitak et al., 2011). The group of Wangmaung developed a QCM genosensor immobilizing 18s rRNA gene for diagnosing mixed malaria using 67 febrile blood samples (Wangmaung et al., 2014). Ittarat and collaborators implemented a QCM genosensor able to differentially diagnose blood infected with P. falciparum from that infected with Plasmodium vivax (P. vivax) in 30 suspected samples (Ittarat et al., 2013). Lee and coauthors exploited SELEX technology and realized a DNA aptasensor targeting Plasmodium lactate dehydrogenase with a detection limit of 1 pM. The device was also able to distinguish between P. vivax and P. falciparum (Lee et al., 2012). In conclusion, QCM can shed light on the mechanisms of malaria ( Joergensen et al., 2010) and enables diagnosis, monitoring, and surveillance of malaria in a cost-effective way. Tuberculosis is a widespread infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), typically involving the lungs. Hiatt and Cliffel developed a QCM immunosensor coated with anti-H37Rv antibodies for both detection of both whole M. tuberculosis bacilli and a surface antigen, lipoarabinomannan (Hiatt & Cliffel, 2012).

11. CANCER The group of Neilson exploited QCM for assessing the carcinogenesis and mutagenesis activity of e-cigarette aerosols, monitoring the effect over time (Adamson et al., 2014; Kilford et al., 2014; Neilson et al., 2015; Thorne & Adamson, 2013; Thorne et al., 2015), while Endes and collaborators simulated the effect of inhalation of high aspect ratio nanoparticles (Endes et al., 2014). Choi and coworkers examined the cytotoxicity effect of the serum protein-coated gold nanoparticles (AuNPs) (Choi et al., 2012). Further, QCM can be exploited for measuring drug concentrations, such as paclitaxel (Pastorino, Caneva Soumetz, Giacomini, & Ruggiero, 2006) or assessing the interactions between drugs (Huska et al., 2009), and shedding light on the mechanisms of antiblastic drugs (Zwang, Singh, Johal, & Selassie, 2013), such as ginsenoside adryamicin (Zhou, Marx, Dewilde, McIntosh, & Braunhut, 2012), nocodazole and taxol (Marx, Zhou, Montrone, McIntosh, & Braunhut, 2007), among others. QCM was also employed for investigating the effect of an anti-oxidant, potential antiblastic

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compound, such as resveratrol (Tili & Michaille, 2011; Zhang et al., 2015) (Table 4). Moreover, QCM-based nanosensor can play a major role for early cancer detection (Huber, Lang, Zhang, Rimoldi, & Gerber, 2015). Breast cancer represents a highly prevalent type of cancer (Arif et al., 2014; Valle, Tramalloni, & Bragazzi, 2015). Abdul Rasheed and Sandhyarani were able to detect attomolar concentration of a gene involved in the pathogenesis of breast cancer, namely breast cancer 1 or BRCA1 gene, using an EQCM genosensor coated with mercaptopropionic acid, polyethylene glycol (PEG), and conjugated with functionalized gold nanoparticles. The detection limit was of 50 attomolar DNA target (294.8 attogram BRCA1gene/mL) (Abdul Rasheed & Sandhyarani, 2014). Another cancer in which early detection and screening programs are important is cervical cancer (Valle et al., 2015). Prakrankamanant and collaborators coupled QCM with loop-mediated isothermal amplification, for detection of high-risk human papillomavirus viral DNA type 58 (HPV-58). The assay was able to detect down to 100 cells (Prakrankamanant et al., 2013). Another preventable cancer is colorectal cancer (Valle et al., 2015). Table 4 Applications of QCM for Early Cancer Detection Cancer References

Breast cancer

Abdul Rasheed and Sandhyarani (2014), Arif et al. (2014), Shen et al. (2007), Zhang, Bai, Luo, Yang, and Cai (2014), and Zhou et al. (2012)

Cervical cancer

Prakrankamanant et al. (2013)

Colorectal cancer

Wu et al. (2013)

Head and neck cancer

Bragazzi, Pechkova, and Nicolini (2014), Bragazzi, Spera, Pechkova, and Nicolini (2014), and Garai-Ibabe et al. (2011)

Lung cancer

Chen, Huang, Shi, and Mu (2011) and Kim et al. (2009)

Melanoma

Fohlerova´, Skla´dal, and Tura´nek (2007)

Metastatic tumors

Saint-Guirons and Ingemarsson (2012)

Ovarian cancer

Chen, Huang, Shi, Mu, and Lv (2012)

Pancreatic cancer Bianco et al. (2013) Prostate cancer

Uludag and Tothill (2012)

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Wu and colleagues were able to detect and identify circulating tumor cells (CTCs) originating from colorectal cancer in blood samples using QCM coated with an anti-EpCAM functionalized supported lipid bilayer (Wu et al., 2013). Isolation and characterization of CTCs represent indeed a significant step toward refinement of the surgical and chemotherapeutic treatment of colorectal cancer (Negin & Cohen, 2010). Other applications of QCM-based sensor for early detection of cancer concern pancreatic cancer (Limani et al., 2015; Partensky, 2015). Bianco and collaborators fabricated a QCM-D aptasensor coated with a selfassembled alkanethiol monolayer and synthetic α-enolase peptides for pancreatic ductal adenocarcinoma detection using patient sera (Bianco et al., 2013). Ozaki and coauthors studied the interaction between serine protease inhibitor, Kazal type 1 (SPINK1), and epidermal growth factor using QCM technique. Melanoma (Higgins, Lee, Galan, & Leffell, 2015) is another cancer that can be studied using QCM (Fohlerova´ et al., 2007). Yildiz and collaborators used different sensors coated with tyrosinase and conjugated with nanoparticles for detecting melanoma cancer cells, coupling these investigations with QCM. Also metastatic tumors can be investigated via QCM (Saint-Guirons & Ingemarsson, 2012).

12. CHRONIC DEGENERATIVE DISEASES Chronic degenerative diseases include cardiovascular diseases, stroke, chronic respiratory diseases (such as asthma and chronic obstructive pulmonary diseases) and diabetes, besides cancer. They carry a tremendous socioeconomic burden, representing the major cause of death worldwide. Eighty percent of deaths occur in developing countries, where mortality for chronic degenerative diseases is approximately double with respect to the number of deaths from all communicable diseases (including HIV/AIDS, tuberculosis, and malaria), maternal and perinatal conditions, and nutritional deficiencies combined (Singhal, 2014; WHO, 2005). Correct lifestyles, such as diet, physical activity, behavior change, body weight control, can prevent noncommunicable diseases (Kushner & Sorensen, 2013), as well as an early detection and diagnosis. The group of Arnold implemented a general-purpose QCM aptasensor for the detection of kallikrein-related peptidase 6, a serine protease involved in different neurodegenerative disorders and certain types of cancer (Arnold et al., 2012).

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Atherosclerosis represents a primary cause of premature death in developed countries. It is a complex, multifactorial pathology due to the interaction of different factors: exposure to environmental chemical mutagens or to environmental physical mutagens, and to a less extent the genetic make up (De Flora et al., 1997; Pulliero et al., 2015). The retention of low-density lipoprotein particles is thought to be the main cause of the development of atherosclerotic plaques. The group of D’Ulivo realized a continuous flow QCM-based device for interaction studies between apolipoprotein B-100 (apoB-100) peptide fragments and some components of the extracellular matrix (namely, collagen I and chondroitin 6-sulfate) (D’Ulivo, SaintGuirons, Ingemarsson, & Riekkola, 2010), trying to shed light on the molecular events of atherosclerosis. Witos and collaborators implemented a QCM coated with collagen types I and III interacting with apoB-100 (Witos, Saint-Guirons, Meinander, D’Ulivo, & Riekkola, 2011). Lipponen and coauthors fabricated a continuous flow QCM for investigating the interactions of heparin with selected peptide fragments of apoB-100 and apoE (Lipponen et al., 2012). Diabetes is becoming increasingly prevalent worldwide, and is associated with an increased incidence and mortality from many cancers (Gallagher & LeRoith, 2015). Saraog˘lu and coauthors coupled QCM with sophisticated biostatistical techniques, such as artificial neural networks for detecting and predicting the HbA1C and blood glucose level (Saraog˘lu, Temurtas, & Altıkat, 2013). Shen and collaborators developed a QCM immunosensor coated with single-chain fragment variable antibodies (B-66, D-23, and L-21) for detecting the phase I enzyme known as cytochrome P450 1B1 (CYP1B1), which is involved in the metabolism of endogenous and exogenous compounds, including carcinogens and is upregulated in a wide variety of noncommunicable diseases. The assay was characterized by a detection limit of 2.2  0.9 nM (Shen et al., 2007). Alzheimer is a neurodegenerative disease putatively induced by membrane-associated amyloid aggregates. QCM can be effectively used to investigate interaction between small molecules and amyloid aggregation, suggesting new drugs. Mustafa and coworkers developed a QCM immunosensor for detecting β-amyloid peptide (Aβ(1–16)) in a range of concentrations 5 μg/mL to 0.05 ng/mL (Mustafa et al., 2010). Okuno and collaborators were able to detect small fragment peptides, such as the pentapeptide KLVFF, finding that 1 Hz frequency decrease corresponded to 30 pg mass increase (Okuno, Mori, Jitsukawa, Inoue, & Chiba, 2006). Kotaker and Moss exploited QCM coated with phospholipid bilayers to

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shed light on the growth of Aβ(1–40) aggregation, being able to prove that dietary intake of polyunsaturated fatty acids may reduce risk of Alzheimer (Kotarek & Moss, 2010). The group of Jaruszewski investigated with QCM-D the effect of nanoparticles-IgG4.1 loaded with imaging contrast agents, and anti-inflammatory and anti-amyloidogenic agents ( Jaruszewski et al., 2014). Osteoarthritis is a chronic degenerative disorder characterized by cartilage loss (Das & Farooqi, 2008). Current diagnostic methods include magnetic resonance imaging and enzyme-linked immunosorbent assay (ELISA), but time-consuming. Wang and collaborators used a continuous flow QCM immunosensor for detection of cartilage oligomeric matrix protein (COMP) in urine samples from 41 volunteers. The researchers were able to detect COMP concentration down to 50 ng/mL (Wang et al., 2010).

13. OCCUPATIONAL HYGIENE Occupational hygiene, born from the convergence of occupational medicine and preventive medicine and hygiene, deals with the protection, safety, and health of workers from work-related hazards. Kosaki and collaborators fabricated carbon nanocage-embedded nanofibrous film for detecting carcinogen aromatic amines (such as aniline). The authors suggested that this approach should be useful for performing cancer risk management at workplace (Kosaki et al., 2013). Wang and colleagues implemented a QCM sensor coated with polyaniline nanofibers for detecting a strong endocrine disruptors, dibutyl phthalate, used as plasticizer and additive to adhesives, printing inks, and nail polishes. The device was characterized by a detection limit of 20 ppb (Wang et al., 2013). The group of O’Brien developed a QCM for measuring the size distribution and concentration of the aerosol produced during the high speed grinding of gray iron castings (O’Brien, Baron, & Willeke, 1986).

14. WATER SAFETY Clean drinking water, adequate sanitation, and hygiene represent one of the Millennium Development Goals. However, 748 million people still lack access to safe drinking water and it is estimated that 1.8 billion people use a source of drinking water that is fecally contaminated (UN, 2014). The problem of access to safe water is still actual and urgent (Kumar et al., 2014).

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QCM can be useful in detecting water-borne pathogens. He and colleagues exploited a QCM sensor coated with self-assembled molecularly imprinted polymers for the detection of trace microcystin-LR in drinking water with a detection limit of 0.04 nM (He et al., 2015), while the group of Poitras implemented a QCM-D for detection of viable Cryptosporidium parvum in water matrices (Poitras et al., 2009). Drinking water contaminated with Cryptosporidium is a serious health issue because the protozoan is highly infecting even at low doses and particularly resistant to chlorine disinfection (Bridle et al., 2012). Another investigated water-borne pathogen is Vibrio cholerae, which causes cholera, a severe acute, secretory diarrhea (Harris, LaRocque, Qadri, Ryan, & Calderwood, 2012; Sigman & Luchette, 2012). Fraser and collaborators used a QCM coated with lipid liquid crystalline submicrometer particles (termed as cubosomes) for detection of Cholera toxin B protein subunit (Fraser et al., 2012). The group of Janshoff exploited QCM coated with gangliosides (GM1, GM3, GD1a, GD1b, GT1b, and asialo-GM1) layers to detect different microbic toxins, including cholera, tetanus, and pertussis toxin ( Janshoff et al., 1997). Alfonta and colleagues fabricated an EQCM coated with horseradish peroxidase and GM1 ganglioside layers for detecting cholera toxin with a sensitivity of 1.0  1013 M (Alfonta et al., 2001). Stine and collaborators developed QCM coated with heat-stabilized glycosphingolipid (GM1) with a detection limit of 0.5 μg/mL (Stine et al., 2004). Matsubara and colleagues fabricated a QCM coated with gangliosides selected from a phage library for sensitive detection of Cholera toxin (Matsubara et al., 1999). The group of Carter fabricated a QCM immunosensor for the rapid detection of V. cholerae serotype O139 with a detection limit of 105 cells/mL (Carter et al., 1995). Fung and coauthors realized a QCM sensor coated with SAM of alkanethiols for detecting Salmonella paratyphi A with a detection limit of 1.7  102 cells/mL. Ma and collaborators investigated the antibiofouling properties of copolymers of methyl methacrylate and acrylate with poly(ethylene oxide-co-ethylene carbonate) with QCM-D. Ogawa and Sugimoto conceived a QCM sensor for the detection of odorous materials (such as gasoline, kerosene, or benzene) in water drawn from rivers (Ogawa & Sugimoto, 2002). Suedee and collaborators implemented a QCM sensor coated with a trichloroacetic acid-molecularly imprinted polymer for the detection of haloacetic acids disinfection by-products in drinking water with a detection limit of 20–50 μg/L (Suedee et al., 2006). Liu

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Table 5 An Overview of Water/Food Samples Analyzed with QCM Sensors Water and Food Samples Analyzed References

Fish

Dai et al. (2014)

Fruit

March et al. (2009)

Meat

Sankaran et al. (2011) and Su and Li (2005a, 2005b)

Milk

Crosson and Rossi (2013), Han et al. (2009), Jin et al. (2009), Mafi and Pelton (2015), Murray and Deshaires (2000), and Szalontai, Ada´nyi, and Kiss (2014)

Vegetables

Sasaki, Noel, and Ring (2008) and Truong et al. (2010)

Water

Bielefeldt, Stewart, Mansfield, Scott Summers, and Ryan (2013), Bridle et al. (2012), Carrington et al. (2006), Cygan et al. (1999), He et al. (2015), Huang et al. (2015), Imai et al. (2011), Jin et al. (2013), Liu et al. (2014), Ogawa and Sugimoto (2002), Suedee et al. (2006), Ueyama et al. (2002), Switzer et al. (2006), Teh, Li, and Yau Li (2014), Timofeyenko et al. (2007), Uttenthaler et al. (2001), Xia et al. (2011), and Ziya Oztu¨rk et al. (2009)

and collaborators fabricated a lab-on-chip biosensor based on coupling QCM with a cell culturing well for rapid screening of different toxicants, such as ammonia, nicotine, aldicarb, in drinking water (Liu et al., 2014). Cygan and collaborators realized a QCM coated with p-tert-butylcalix[4] arenetetrathiolate monolayers for their in situ aqueous chemical sensing of contaminants, and in particular of alkylbenzenes (Cygan et al., 1999) (Tables 5 and 6).

15. VETERINARY PUBLIC HEALTH QCM can be successfully used also in the field of Veterinary Public Health, for detecting pathogens, such as Edwardsiella tarda (Choi & Choi, 2012) or African swine fever virus, a large, double-stranded DNA virus, which infects domestic pigs, warthogs, and bushpigs, and is transmitted by arthropods, namely soft ticks of the genus Ornithodoros. The group of Uttenthaler constructed a QCM immunosensor coated with peptidespecific monoclonal antibody 18BG3 for the detection of the virus protein

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Table 6 An Overview of Chronic Degenerative Diseases that can be Investigated Using QCM Sensors Chronic Degenerative Disease References

Alzheimer

Buell, Dobson, and Welland (2012), Jaruszewski et al. (2014), Kotarek and Moss (2010), Mustafa et al. (2010), and Okuno et al. (2006)

Amyloidosis

Obayashi and Ando (2008)

Asthma and other chronic obstructive pulmonary diseases

Fakhrullin et al. (2007) and Thorne and Adamson (2013)

Atherosclerosis

D’Ulivo et al. (2010), Lipponen et al. (2012), and Witos et al. (2011)

Diabetes

Buell et al. (2012), Luo et al. (2006), Saraog˘lu et al. (2013), and Shen et al. (2007)

Osteoarthritis

Wang et al. (2010)

Parkinson

Arnold et al. (2012), Buell et al. (2012), Daturpalli, Waudby, Meehan, and Jackson (2013), Hellstrand et al. (2013), and Mustafa et al. (2010)

Retinal degeneration and macular Tezcaner et al. (2006) degeneration Rheumatoid arthritis

Drouvalakis et al. (2008)

Systemic lupus erythematosus

Fakhrullin et al. (2007)

73 (VP73) with a limit of detection of 0.31–1 μg/mL (Uttenthaler et al., 1998). Lee and Chang developed a QCM immunosensor for real-time determination of cattle bovine ephemeral fever virus, working well as ELISA (Lee & Chang, 2005). Another application is given by Vibrio harveyi, a pathogenic bacteria causing morbidity and mortality in commercial shrimp cultures, detected by a functionalized QCM immunosensor in a range of 103–107 CFU/mL (Buchatip et al., 2010). Ramos-Jesus and coworkers developed a QCM immunosensor for the diagnosis of the canine visceral leishmaniasis using a recombinant antigen of Leishmania chagasi (rLci2B-NH6) (Ramos-Jesus et al., 2011). Souto and

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collaborators detected Leishmania employing QCM, using as aptamer a recombinant chimeric protein (CP10) (Souto et al., 2015).

16. FOOD HYGIENE Food hygiene is a priority in public health and plays a major role within the one medicine framework, an approach which involves the collaboration of veterinary medicine, thus linking human diseases, animal diseases, and food-safety assurance (Trevisani & Rosmini, 2008; Wielinga & Schlundt, 2013). Food safety hazards can be subdivided into biological (macrobiological/ microbiological), chemical, physical/extraneous material, allergenic, nutritional, and novel foods/biotechnology-related hazards. Microbiological hazards represent an increasingly important public health issue, in that they cause food-borne diseases. The WHO defines food-borne illnesses as “diseases, usually either infectious or toxic in nature, caused by agents that enter the body through the ingestion of food” (Velusamy, Arshak, Korostynska, Oliwa, & Adley, 2010; WHO, 2002). Recently, new serious hazards have emerged in the food chain, such as enterohemorrhagic E. coli and bovine spongiform encephalopathy (O’Brien, 2012). Each year in the United States, some 76 million people experience food-related illnesses, while in the UK food-related diseases account for about 10% of morbidity and mortality and cost the NHS about 6 billion pounds annually. It is estimated that 1 billion people worldwide experience at least one episode of food-borne illness (King, 2012). Chemical hazards remain a significant source of food-borne illness in our industrial globalized society, affecting both food production and food delivery and supply. Chemical and biological contaminants in food include natural toxicants, such as mycotoxins (aflatoxin, deoxynivalenol or vomitoxin, ochratoxin A, fumonisin, and patulin, among others), marine toxins (like microscopic marine algae), other natural toxins (like glycoalkaloids, furocoumarins, or goitrogens), and environmental contaminants, such as arsenic, mercury, lead, and other heavy metals. Other chemical toxicants can form during the process of food production and manipulation, the so-called processing-induced chemicals, such as acrylamide, ethyl carbamate, and furan. Food additives, micronutrients, agricultural products, pesticides, and veterinary drugs can be used in the food chain. Physical hazards derive from unsanitary conditions during the production, manipulation, and delivery of food.

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Allergenic hazards are caused by allergens, which are proteins able to trigger an abnormal immune response in sensitive subjects. Some common allergens include peanuts, tree nuts, sesame seeds, milk, eggs, seafood, soy, and wheat. Nutritional hazards can come from a process termed as food fortification, when not performed in a proper way. Usually, food fortification has the objectives of replace nutrients lost during the phases of food production and distribution, to ensure adequate nutritional level both for healthy subjects and for individual with special dietary needs. Both underfortification and overfortification can be dangerous for health. Finally, novel foods/biotechnology-related hazards occur when food results from a not conventional or unsafe production process or when foods have been genetically modified and manipulated.

16.1 Food-borne pathogens Uzawa and collaborators developed a QCM sensor coated with a thin film of globobiosyl (Gb2) ceramide to detect Shiga toxins (Uzawa et al., 2002). Salmonella is a rod-shaped bacillus of the Enterobacteriaceae family. It is distributed worldwide in animals and in the environment. It causes illnesses such as typhoid fever, paratyphoid fever, and food poisoning. Ozalp and colleagues developed a QCM coupled with an aptamer-based magnetic bead prepurification system for the detection of Salmonella enterica serovar typhimurium cells in food samples (mainly milk). The limit of detection was of 100 CFU/mL (Ozalp et al., 2015). Salam and collaborators developed a QCM immunosensor conjugated with gold nanoparticles and coupled with a microfluidic system for the detection of S. typhimurium. The limit of detection was of 10–20 CFU/mL (Salam et al., 2013). Sankaran and collaborators developed a modified QCM coated with engineered insect odorant-binding proteins to detect volatile organic compounds (VOCs) indicative to Salmonella contamination in packaged beef, such as 3-methyl-1-butanol and 1-hexanol (Sankaran et al., 2011). Babacan and colleagues designed a QCM coated with protein A antibody for detecting S. typhimurium (Babacan et al., 2002). E. coli is a Gram-negative, facultatively anaerobic, and rod-shaped bacterium. E. coli can be subdivided into enterotoxigenic (a major cause of travelers’ diarrhea and infant diarrhea in developing countries), enteroinvasive (a cause of dysentery), enteropathogenic (a cause of infant diarrhea), and enterohemorrhagic (a cause of hemorrhagic colitis and hemolytic uremic

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syndrome) (Levine, 1987). Kim and Park implemented a flow-type QCM biosensor for the detection of different E. coli strains in food samples, with a linear sensor response in double-logarithmic scale in the range 1.7  105 to 8.7  107 CFU/mL (Kim & Park, 2003). Wu and coauthors developed a thiol-modified DNA-based QCM for real-time detection of E. coli O157:H7 gene eaeA in a circulating-flow system (Wu et al., 2007). Jiang and collaborators developed a QCM immunosensor conjugated with micro/nanobeads for the detection of E. coli O157:H7 ( Jiang et al., 2011). Guo and coworkers fabricated a QCM immunosensor conjugated with nanoparticles for detection of E. coli O157:H7 with a detection limit of 0–1 log CFU/mL (Guo et al., 2012). The group of Han developed a QCM sensor coated with di-para-xylene (parylene) for detection of different microrganisms, including E. coli, with a detection limit of 102 cells/mL (Han et al., 2009). Mao and coworkers fabricated a QCM genosensor conjugated with Fe3O4 nanoparticles with a detection limit of 2.67  102 CFU/mL (Mao et al., 2006). Poitras and Tufenkji designed a QCM genosensor able to detect E. coli O157:H7, E. coli K12, D21, and Bacillus subtilis (Poitras & Tufenkji, 2009). Su and Li fabricated a QCM immunosensor able to detect the bacteria in the range of 103–108 CFU/mL (Su & Li, 2004a, 2004b, 2005a, 2005b). In conclusion, QCM is effective for shedding light on the mechanisms of the pathogen (Gutman et al., 2013; Inomata et al., 2013; Latif et al., 2014; Tijing et al., 2011). Campylobacter jejuni (C. jejuni) is a curved, helical-shaped, nonsporeforming, Gram-negative, and microaerophilic bacterium, one of the most common causes of human gastroenteritis in the world. Yakovleva and coworkers designed a QCM coated with lectins for the detection of C. jejuni (Yakovleva et al., 2011). Safina and coauthors realized a QCM coated with lectins with a detection limit of 103 cells and a dynamical range of 103–2  104 cells (Safina et al., 2008). QCM is able to detect C. jejuni reasonably well as other kinds of sensors (Yang et al., 2013). Bacillus cereus (B. cereus) is a Gram-positive, rod-shaped bacterium, which causes the classical “fried rice syndrome.” Its virulence factors include cereolysin and phospholipase C. QCM enables to study in details its mechanism of adhesion and growth (Olofsson et al., 2005), as well as allows to quickly detect it. For example, Vaughan and collaborators realized a QCM immunosensor with a limit of detection of about 104 cells/mL (Vaughan et al., 2003). Susmel and coworkers developed a QCM immunosensor for detecting B. cereus enterotoxin with a detection limit of about 0.6 ng/mL (Susmel et al., 2005). Finally, the group of Wirtanen developed

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an interesting, experimental sanitation protocol for detecting B. cereus in dairies (Wirtanen et al., 2002). Listeria monocytogenes is a Gram-positive, facultative anaerobic, and common food pathogen. Vaughan and collaborators developed a QCM immunosensor coated with a SAM of thiosalicylic acid with a detection limit of 1  107 cells/mL (Vaughan et al., 2001). Clostridium perfringens is a Gram-positive, rod-shaped, anaerobic, and spore-forming bacterium. Cai and colleagues developed a QCM genosensor conjugated with gold nanoparticles with a limit of detection of 1.5  102 to 1.5  108 CFU/mL for bacteria. Finally, Salmain and collaborators developed a QCM-D based immunosensor for the detection and quantification of staphylococcal enterotoxin A. The detection limit was of 20 ng/mL (Salmain et al., 2011).

16.2 Mycotoxins Fungal infestation on wheat is a severe nutritional problem. Some species of mold, in particular Fusarium species, may release harmful and toxic metabolites, such as deoxynivalenol, which belongs to the B trichothecene family of mycotoxins and is a strong inhibitor of protein and DNA synthesis. Further, it is characterized by immunosuppressive and cytotoxic effects. Human exposure to such toxins causes vomiting and gastroenteritis, dermatitis, cough, and rhinitis (Wang, Nagarajan, et al., 2014; Wang, Wu, et al., 2014). Other fusarotoxins include Fumonisin B1, zearalenone, nivalenol, and T-2 toxins (Escriva´, Font, & Manyes, 2015). Eifler and collaborators designed a sort of electronic nose, modifying a QCM sensor and making it able to detect VOCs released by F. cerealis, F. graminearum, F. culmorum, and F. redolens in wheat grains, with an accuracy higher than 80% (Eifler et al., 2011).

16.3 Allergens Dai and coworkers designed a biosensor for rapidly detecting histamine in foods, like spiked fish products. The detection limit was of 7.49  104 mg/kg (Dai et al., 2014). Kong and coworkers designed a molecularly imprinted QCM sensor conjugated with self-assembled gold nanoparticles for detecting ractopamine in spiked swine feed samples. The detection limit was of 1.17  106 mol/L (Kong et al., 2014). Yola and coworkers implemented a QCM sensor for the real-time detection of tobramycin in food samples, such as chicken egg white and milk. The limit of detection € was of 5.7  1012 M (Yola, Uzun, Ozaltın, & Denizli, 2014). Peeters

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and coworkers designed a QCM aptasensor, exploiting the heat-transfer method technology for detecting the peanut allergen Ara h 1 in a food matrix of dissolved peanut butter (Peeters et al., 2015). Chu and colleagues designed a QCM coupled with gold nanoparticles for detecting gliadin. The limit of detection was 8 ppb (Chu, Lin, Chen, Chen, & Wen, 2012).

16.4 Pesticides and Other Chemical Components A variety of QCM sensors for detection of pesticides, such as atrazine, in food samples have been implemented (Hromadova´ et al., 2013; Jia et al., 2012; Marrazza, 2014; Pogorelova et al., 2002). Gao and collaborators implemented a QCM sensor based on molecularly imprinted ultra-thin films for detecting profenofos in food samples (Gao et al., 2012). Toniolo and colleagues developed a QCM coated with room temperature ionic liquids (RTILs) containing imidazolium or phosphonium cations for the analysis of flavors in food samples. The device was able to successfully analyze and distinguish up to 31 VOCs, such as alcohols, phenols, aldehydes, esters, ketones, acids, amines, hydrocarbons, and terpenes (Toniolo et al., 2013).

16.5 Micronutrients The regular consumption of foods containing probiotic bacteria, such as Bifidobacterium bifidum O1356 and Lactobacillus acidophilus O1132, have beneficial physiological effects on the health, in particular on oral health and the digestion system (Pandey, Berwal, Solanki, & Malik, 2015). Szalontai and collaborators designed a QCM-based immunosensor for the quantification of probiotics in fermented milk (Szalontai et al., 2014).

16.6 Genetically Modified Organisms Another concern is given by genetically modified organisms (GMOs). Mannelli and collaborators developed a QCM coated with ssDNA probes immobilized using a thiol-dextran procedure or a thiol-derivatized probe and blocking thiol procedure (Mannelli, Minunni, Tombelli, & Mascini, 2003). Truong and collaborators developed a DNA EQCM based on MWCNTs-doped polypyrrole (Truong et al., 2010). Passamano and Pighini developed a QCM sensor based on a biotinylated Cry1A(b) gene fragment probe, since Cry1A(b) is characteristic of GMO phenotypes and

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has been authorized and included in the Community Register of Genetically Modified Food and Feed, in order to control insect resistance (Passamano & Pighini, 2006).

17. ENVIRONMENTAL MONITORING QCM immunosensors can be exploited also for environmental monitoring, even though so far there are still few applications in this field with respect to other kinds of biosensors and nanosensors (Farre´ et al., 2010). Kurosawa and collaborators managed to successfully immobilize antibodies on the QCM surface, by SAM technique and artificial phospholipid (2-methacryloyloy ethyl phosphorylcholine) polymer conjugated with antibisphenol-A antibodies, testing it on fly ash extracted samples of dioxins. The detection limit was of 0.1–0.01 ng/mL (Kurosawa et al., 2006). Park and colleagues exploited the QCM technology for the detection of 2,3,7,8-tetrachloro-p-dibenzodioxin (TCDD) using IgG1 and IgM MAbs with mono 6-(2,3,6,7-tetrachloroxanthene-9-ylidene) hexyl succinate as a hapten conjugated with bovine serum albumin (Park et al., 2006). Kurosawa and coauthors developed a QCM immunosensor for the detection of TCDD in environmental samples, such as fly ash samples from municipal solid wastes (Kurosawa et al., 2005). Mascini and coworkers used a QCM coated with pentapeptides generated with molecular modeling software for detecting dioxin in solid–gas analysis (Mascini et al., 2004). Zhou and Cao implemented a QCM immunosensor for the detection of different polychlorinated dibenzo-p-dioxins with detection limit in the concentration range 0.01–1.3 ng/mL (Zhou & Cao, 2001). Park and coauthors designed a piezoelectric sensor for the detection of 2,4-dinitrophenol (Park et al., 2003). Boujday and colleagues designed a QCM sensor for detection of polycyclic aromatic hydrocarbons deriving from the incomplete combustion of organic compounds, present in the urban air, water, soil, and foodstuff with a sensitivity of 5 μM (Boujday, Nasri, Salmain, & Pradier, 2010). Nicolini and coworkers designed an in-house nanogravimetric device for detecting carbon dioxide (Nicolini, Adami, et al., 2012; Nicolini, Bragazzi, et al., 2012; Terencio, Bavastrello, & Nicolini, 2012). Shinar and collaborators realized a QCM coated with graphite microparticles for detection and monitoring of toluene and other VOCs as an

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alternative to polymeric coatings with low glass transition temperatures (such as poly(isobutylene) and poly(diphenoxyphosphazene)) and high glass transition temperatures (such as polystyrene) polymers (Shinar et al., 2000). The group of Speller fabricated a QCM device able to recognize up to 18 different organic vapors (alcohols, hydrocarbons, chlorohydrocarbons, and nitriles) (Speller et al., 2015).

18. BIOHAZARDS AND BIOTERRORISM Bioterrorism consists in the use of biological agents as weapons. Biosensors appear able to resolve a potentially large number of analytical problems and challenges in the area of defense and homeland security (Ghosh et al., 2011; Kirsch, Siltanen, Zhou, Revzin, & Simonian, 2013). Researchers use biological warfare surrogates, like Bacillus thuringiensis (Thammasittirong et al., 2011), which are applied extensively in the United States to control the gypsy moth, Lymantria dispar (Van Cuyk et al., 2011), Bacillus atrophaeus (B. atrophaeus) (Gottfried, 2011), or B. subtilis (Ghosh et al., 2011; Jenkins et al., 2004; Lee, 2005). B. atrophaeus is another surrogate. Alava and collaborators fabricated a QCM immunosensor with a detection limit of 1.4  106 spores/mL (Alava et al., 2009). The group of Farka developed a QCM immunobiosensors with a limit of detection of 106 CFU/mL (Farka et al., 2013). The rapid detection of Bacillus anthracis (B. anthracis), a Gram-positive, spore-forming, nonmotile bacterium, the causative agent of anthrax disease, has fostered a lot of studies and researches, since the bioterrorism attacks in the United States in September 2001, in New York and in South Florida (Goel, 2015). Hao and collaborators developed a QCM immunosensor for the rapid detection of B. anthracis spores and vegetative cells, coating the electrode surface with protein A on a mixed SAM of 11-mercaptoundecanoic acid and 6-mercaptohexan-1-ol. The detection limit of the assay was of 103 CFU or spores/mL (Hao et al., 2009). The same group implemented a further QCM biosensor based on the thiol-DNA probes of the 168 bp fragment of the Ba813 gene in chromosomes and the 340 bp fragment of the pag gene in plasmid pxO1 of B. anthracis. The limit of detection was of 3.5  102 CFU/mL of B. anthracis vegetative cells (Hao et al., 2011). The group of Oztuna fabricated an aminated-poly(vinyl chloride)-coated QCM immunosensor for simultaneous rapid detection of B. anthracis spores (Oztuna & Nazir, 2012; Oztuna et al., 2014).

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Francisella tularensis (F. tularensis), a very small, Gram-negative microbe, causes the rabbit fever or tularemia. Kleo and collaborators designed an immunosensor coupled with microfluidics for the detection of F. tularensis. The detection limit was of about 4  103 CFU/mL (Kleo et al., 2012). Pohanka and Skladal developed a QCM immunosensor with a limit of detection of 5  10 Ft cells/mL (Pohanka & Skladal, 2005, 2007). The same group designed a piezoelectric biosensor for diagnosis of infection by F. tularensis subsp. holarctica in European brown hares (Lepus europaeus). In conclusion, QCM is very effective in detection and diagnosis of F. tularensis (Pohanka, 2009).

19. HOMELAND SECURITY The detection of explosives and explosive-related compounds is crucial for homeland security and counter-terrorism applications (Caygill, Davis, & Higson, 2012). The group of Procek fabricated a QCM coated with TiO2 nanostructures for detecting low NO2 and explosive vapors (such as nitroglycerine vapors) (Procek, Stolarczyk, Pustelny, & Maciak, 2015). Pei and collaborators developed a QCM sensor coated with ZnO-modified manganese dioxide nanofibers for detection of dimethyl methylphosphonate, an organophosphonate compound structurally related to sarin. Apodaca and collaborators implemented a QCM sensor coated with a 2D molecularly imprinted butanethiol monolayers (2D MIMs) for the detection of different nitroaromatic compounds (in particular, 2,4-dinitrotoluene or DNT, a precursor of the explosive 2,4,6-trinitrotoluene or TNT) (Apodaca, Pernites, Del Mundo, & Advincula, 2011). Vaiyapuri and colleagues designed a QCM coated with dithiol-functionalized pyrene derivatives for the detection of explosive nitroaromatic compounds (including DNT) (Vaiyapuri et al., 2011). The group of Cerruti designed a QCM coated with a polymeric matrix composed of poly(ethylene-co-glycidyl methacrylate) for detection of TNT and DNT (Cerruti et al., 2009). Palmas and coauthors fabricated a QCM coated with functionalized polysiloxane polymers for detection of explosive gases (Palmas et al., 2013). Finally, Rehman and coworkers fabricated a QCM coupled with a chemoselective RTIL for detecting explosive gases, such as nitromethane and 1-ethyl-2-nitrobenzene with detection limit of <0.2% (Rehman & Zeng, 2012; Rehman et al., 2011).

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20. CONCLUDING REMARKS QCM holds great promises for public health, since it enables the rapid, online, in situ microbial detection (Skottrup et al., 2008), the discovery of new antibiotics (Aslan et al., 2012), useful for treating drug-resistant microorganisms, as well as food-safety and hygiene assurance, being able to detect food-borne pathogens, contaminants, GMOs, and other food hazards (Michelini, Simoni, Cevenini, Mezzanotte, & Roda, 2008). Further, it detects viruses of relevant burden for public health, such as influenza and HBV (Yao & Fu, 2014), as well as it senses pathogens involved in STDs, tropical, and neglected tropical infections, invasive diseases, and nosocomial infections. QCM has proven extremely useful also in the field of carcinogenesis, occupational hygiene, and environmental monitoring (Kurosawa et al., 2006; Wanekaya, Chen, & Mulchandani, 2008). New sophisticated QCM sensors have been developed, such as phagebased piezoelectric biosensors and aptasensors (Petrenko, 2008). Future efforts are required in order to translate the huge body of knowledge that has exponentially grown in few decades and to make QCM a useful tool at the point-of-care level (Prakrankamanant, 2014).

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