New Psychoactive Substances (NPS) are a global concern since they are spreading at an unprecedented rate. Despite their commerce still being limited compared to traditional illicit drugs, the identification of NPS in seizures may represent a challenge because of the variety of possible structures. In this study we report the successful application of molecular networking (MN) to identify unexpected fentanyl analogs in two seizures. The samples were extracted with 1 mL of methanol and analyzed with an untargeted data-dependent acquisition approach by LC–HRMS. The obtained data were examined using the MN workflow within the Global Natural Product Search (GNPS). A job was submitted to GNPS by including both seizures and standard mixtures containing synthetic cannabinoids and fentanyls raw files; spectra obtained from standards were used to establish representative networks for both molecular classes. All synthetic cannabinoids in the mixture were linked together resulting in a molecular network despite their different fragmentation spectra. Looking at fentanyls, all the molecules with the typical 188.143 and 105.070 fragments were combined in a representative network. By exploiting the standard networks two unexpected fentanyls were found in the analyzed seizures and were putatively annotated as para-fluorofuranylfentanyl and (iso)butyrylfentanyl. The identity of these two fentanyl analogs was confirmed by NMR analysis. Other m/z ratios in the seizures were compatible with fentanyl derivatives; however, they appeared to be minor constituents, probably impurities or synthetic byproducts. The latter might be of interest for investigations of common fingerprints among different seizures.

MT-45 is a synthetic opioid with a pharmacological activity comparable to morphine and it has been involved in intoxications and fatalities reported in Europe and in USA. It was recently subject to control measures, but to date the metabolic pathways of the substance are still unknown. Using rat hepatocytes and LC-HRMS, 14 novel Phase I and II MT-45 metabolites were identified, products of monohydroxylation, dihydroxylation and N-dealkylation; glucuronide conjugation of mono- and dihydroxylated metabolites also occurred. The detected metabolites were firstly predicted in silico, then incubation of the drug with rat hepatocytes was carried out and the obtained metabolites were identified by LC-HRMS, with retention times, mass shift between theoretical mass and observed mass (<5 ppm), peak abundance and fragmentation pattern. Hydroxylated MT-45 was found to be the major metabolite of MT-45 in vitro experiments. The presence of all metabolites was confirmed by in vivo experiments in urine samples of CD-1 male mice; in these samples hydroxy-MT-45-glucuronide and di-hydroxy-MT-45-glucuronide are the most abundant metabolites, while the parent drug is found at concentration <10 ng mL-1 after 300 min. The knowledge of Phase I and II MT-45 metabolite structure is then crucial to develop analytical methods to identify MT-45 consumption in clinical and forensic testing.

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Vesuvius eruption that destroyed Pompeii in AD 79 represents one of the most important events in history. The cataclysm left behind an abundance of archeological evidence representing a fundamental source of the knowledge we have about ancient Roman material culture and technology. A great number of textiles have been preserved, rarely maintaining traces of their original color, since they are mainly in the mineralized and carbonized state. However, one outstanding textile sample displays a brilliant purple color and traces of gold strips. Since the purple was one of the most exclusive dyes in antiquity, its presence in an important commercial site like Pompeii induces us to deepen the knowledge of such artifacts and provide further information on their history. For this reason, the characterization of the purple color was the main scope of this research, and to deepen the knowledge of such artifacts, the SERS (Surface Enhanced Raman Scattering) in solution approach was applied. Then, these data were enriched by HPLC-HRMS analyses, which confirmed SERS-based hypotheses and also allowed to hypothesize the species of the origin mollusk. In this context, a step-by-step integrated approach resulted fundamental to maximize the information content and to provide new data on textile manufacturing and trade in antiquity.

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Mobile sensor networks enable the monitoring of remote and hostile environments without requiring human supervision. Several approaches have been proposed in the literature to let mobile sensors self-deploy over a region of interest. In this paper we study, for the first time, the vulnerabilities of one of the most referenced approaches to mobile sensor deployment, namely the Voronoi-based approach. We show that, by compromising a small number of sensors, an attacker can influence the sensor deployment causing a significant reduction of the monitoring capability of the network. We propose a secure deployment algorithm called SecureVOR. We formally prove that SecureVOR has guaranteed termination and that it allows legitimate sensors to detect the malicious behavior of compromised nodes. We also show by extensive simulations that SecureVOR is able to fulfill the network monitoring goals even in presence of an attack, at the expense of a small performance overhead. © 2014 IEEE

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