Azithromycin australia br Acknowledgements This work was sup

Acknowledgements This work was supported by grants from the Agencia Nacional de Promoción Científica y Tecnológica (PICT2013-0495, PICT2016-1821), and from CONICET (PIP 2013-562), as well as from Fundación René Barón and Fundación Williams.
Introduction Histamine poisoning, also known as scombroid poisoning, is a food-borne disease that results due to the ingestion of contaminated food, like seafood, cheese, sauerkraut, beer, wine, and processed meat. According to the technical report of European Food Safety Authority (EFSA) 2017 on the assessment of the incidents of Azithromycin australia intoxication during the period 2010–2014, 306 food-borne outbreaks were reported by 12 EU Member State (“Assessment of the incidents of histamine intoxication in some EU countries,”, 2017). Almost 40% of all seafood-related outbreaks reported to US Centers for Disease Control and Prevention (CDC) are due to histamine toxicity (Gould et al., 2013). Although, the ingested histamine is metabolized enzymatically (by diamine oxidase (DAO), monoamine oxidase (MAO) and histamine methyltransferase (HMT) enzymes) in the human gut and converted into less physiologically active products, but the detoxification process becomes insufficient when excess histamine is consumed. As per the US Food and Drug Administration (US-FDA) guidelines (1998), histamine tolerance limit is estimated to be around <50 ppm (Lüthy & Schlatter, 1983). The consumption of higher doses of histamine can lead to some allergic reactions, while the long-term intake can lead to several other toxicological implications such as Alzheimer's disease, asthma, and neuropsychiatric disorders (Bodmer, Imark, & Kneubühl, 1999). Hence, the detection of histamine in food samples is of utmost importance for a significant reduction in the number of food poisoning outbreaks worldwide. Currently, a wide range of technologies are commercially available for the detection of histamine which includes thin layer chromatography (Tao et al., 2011), capillary zone electrophoresis (Vitali et al., 2013), gas chromatography (Hwang, Wang, & Choong, 2003), high-performance liquid chromatography (HPLC) (Chen, Deng, Yang, Wang, & Xu, 2016), colorimetry (Patange, Mukundan, & Ashok Kumar, 2005), fluorimetry (Adamou et al., 2005), electrochemical (Harsing Jr, Nagashima, Vizi, & Duncalf, 1986; Young, Jiang, & Kirchhoff, 2013), and enzyme-linked immunosorbent assay (ELISA) (Serrar, Brebant, Bruneau, & Denoyel, 1995). Although many of these techniques are known to exhibit good sensitivity and specificity, these are often time-consuming and require extensive sample pre-treatment and a trained operator. In addition, field deployability of many of these techniques is not possible (or very limited) due to the requirement of large size and/or sophisticated expensive instruments (Cinquina et al., 2004; Tahmouzi, Khaksar, & Ghasemlou, 2011). Further, the ELISA based detection strategies, which are known for high sensitivity and rapid response, require expensive test kits and their strict storage conditions to ensure the stability of the enzymes and ultimately result in expensive analysis in the laboratory set-up (Jerez, Grassi, & Civera, 1994). Advances in the nanomaterial sciences and their integration with parallelly developed microelectromechanical systems (MEMS)/ nanoelectromechanical systems (NEMS) technologies have significantly improved the performance, such as speed and cost of analysis, field deployability, low sample volume requirement, miniaturized size, etc., of all categories of the sensors (Dirk, Natalia, & Susann, 2012; Hippalgaonkar et al., 2010; Syedmoradi et al., 2017). Examples range from the development and applications of various nanomaterials such as carbon nanotubes (CNT) (Zang, Zhou, Chang, Liu, & Lin, 2015), graphene (Liao & Koide, 2011) and metal nanoparticles (Jian, Huang, & Lu, 2012; Rajdi et al., 2013) to their integration with MEMS/NEMS technologies (Shulan et al., 2015). For example, Prabhakar and Mukherji (2010) reported an analytical chip by utilizing gold nanoparticle (AuNPs) coated on a C-shaped polymer waveguide which was suitable for wide-scale, affinity biosensor based on refractive index (RI) change (Prabhakar & Mukherji, 2010). The miniaturization of the sensors has paved the way for the creation of several field-deployable point-of-care diagnostics. Further, some of these nanosensors have demonstrated the capability of multiplexed detection through the use of uniquely functionalized array of devices that displayed good reusability for the detection of each analyte (Fritz, 2008; Lavrik, Sepaniak, & Datskos, 2004; Waggoner & Craighead, 2007).