Microbial arsenite oxidation is an essential biogeochemical process whereby more toxic arsenite is oxidized to the less toxic arsenate. Thiomonas strains represent an important arsenite oxidizer found ubiquitous in acid mine drainage. In the present study, the arsenite oxidase gene (aioBA) was cloned from Thiomonas delicata DSM 16361, expressed heterologously in E. coli and purified to homogeneity. The purified recombinant Aio consisted of two subunits with the respective molecular weights of 91 and 21 kDa according to SDS-PAGE. Aio catalysis was optimum at pH 5.5 and 50-55 °C. Aio exhibited stability under acidic conditions (pH 2.5-6). The V max and K m values of the enzyme were found to be 4 µmol min(-1) mg(-1) and 14.2 µM, respectively. SDS and Triton X-100 were found to inhibit the enzyme activity. The homology model of Aio showed correlation with the acidophilic adaptation of the enzyme. This is the first characterization studies of Aio from a species belonging to the Thiomonas genus. The arsenite oxidase was found to be among the acid-tolerant Aio reported to date and has the potential to be used for biosensor and bioremediation applications in acidic environments.
Due to the outbreak of the COVID-19 pandemic, practicing personal hygiene such as frequent hand sanitising has become a norm. The making of effective hand sanitiser products should follow the recommended formulations, but the high demand worldwide for such affordable products could have made them a candidate for counterfeiting, thus deserving forensic determination and profiling for source determination or supply chain tracing. In this study, determination and discrimination of hand sanitisers was carried out by employing attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics. Fifty commercially available hand sanitisers were obtained from the market and analysed. ATR-FTIR profiles of each sanitiser were compared and decomposed by principal component analysis (PCA) followed by linear discriminant analysis (LDA). Physical observation enabled the discrimination of seven samples based on their respective colours, the presence of beads and their colours, and the physical forms of formulations. Subsequently, eight distinct patterns were observed through visual comparison of ATR-FTIR profiles of the remaining 43 samples. An initial unsupervised exploratory PCA model indicated the separation of two main groups with ATR-FTIR profiles similar to those of ethanol and isopropanol, respectively. The PCA score-LDA model provided good predictions, with a 100% correct classification into eight different groups. In conclusion, this study demonstrated a quick determination and discrimination of hand sanitiser samples, allowing screening for any restricted components and sample-to-sample comparison.
A portable electrochemical sensor was developed to determine xylazine in spiked beverages by adsorptive stripping voltammetry (AdSV). The sensor was based on a graphene nanoplatelets-modified screen-printed carbon electrode (GNPs/SPCE). The electrochemical behavior of xylazine at the GNPs/SPCE was an adsorption-controlled irreversible oxidation reaction. The loading of graphene nanoplatelets (GNPs) on the modified SPCE, electrolyte pH, and AdSV accumulation potential and time were optimized. Under optimal conditions, the GNPs/SPCE provided high sensitivity, linear ranges of 0.4-6.0 mg L-1 (r = 0.997) and 6.0-80.0 mg L-1 (r = 0.998) with a detection limit of 0.1 mg L-1 and a quantitation limit of 0.4 mg L-1. Repeatability was good. The accuracy of the proposed sensor was investigated by spiking six beverage samples at 1.0, 5.0, and 10.0 mg L-1. The recoveries from this method ranged from 80.8 ± 0.2-108.1 ± 0.3 %, indicating the good accuracy of the developed sensor. This portable electrochemical sensor can be used to screen for xylazine in beverage samples as evidence in cases of sexual assault or robbery.
Drugs-facilitated crimes (DFCs) involve the incapacitation of victims under the influence of drugs. Conventionally, a drug administration act is often determined through the examination of biological samples; however, dry residues from any surface, such as drinking glass if related to a DFC could be a potential source of evidence. This study was aimed to establish an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics for the determination of spiked sedative-hypnotics from dry residues of a drug-spiked beverage. In this study, four sedative-hypnotics, namely diazepam, ketamine, nimetazepam, and xylazine were examined using ATR-FTIR spectroscopy. Subsequently, the ATR-FTIR profiles were compared and decomposed by principal component analysis (PCA) followed by linear discriminant analysis (LDA) for their detection and discrimination. Visual comparison of ATR-FTIR profiles revealed distinct spectra among the tested drugs. An initial unsupervised exploratory PCA model indicated the separation of four main sedative-hypnotics clusters, and the proposed PCA score-LDA model had allowed for a 100% accurate classification. Discrimination of sedative-hypnotics from a dry beverage previously spiked with these drugs was also possible upon an additional extraction procedure. In conclusion, ATR-FTIR coupled with PCA score-LDA model was useful in detecting and discriminating sedative-hypnotics, including those that had been previously spiked into a beverage.