Dataset Information

Green Composite Sensor for Monitoring Hydroxychloroquine in Different Water Matrix.

ABSTRACT: Hydroxychloroquine (HCQ), a derivative of 4-aminoquinolone, is prescribed as an antimalarial prevention drug and to treat diseases such as rheumatoid arthritis, and systemic lupus erythematosus. Recently, Coronavirus (COVID-19) treatment was authorized by national and international medical organizations by chloroquine and hydroxychloroquine in certain hospitalized patients. However, it is considered as an unproven hypothesis for treating COVID-19 which even itself must be investigated. Consequently, the high risk of natural water contamination due to the large production and utilization of HCQ is a key issue to overcome urgently. In fact, in Brazil, the COVID-19 kit (hydroxychloroquine and/or ivermectin) has been indicated as pre-treatment, and consequently, several people have used these drugs, for longer periods, converting them in emerging water pollutants when these are excreted and released to aquatic environments. For this reason, the development of tools for monitoring HCQ concentration in water and the treatment of polluted effluents is needed to minimize its hazardous effects. Then, in this study, an electrochemical measuring device for its environmental application on HCQ control was developed. A raw cork-graphite electrochemical sensor was prepared and a simple differential pulse voltammetric (DPV) method was used for the quantitative determination of HCQ. Results indicated that the electrochemical device exhibited a clear current response, allowing one to quantify the analyte in the 5-65 µM range. The effectiveness of the electrochemical sensor was tested in different water matrices (in synthetic and real) and lower HCQ concentrations were detected. When comparing electrochemical determinations and spectrophotometric measurements, no significant differences were observed (mean accuracy 3.0%), highlighting the potential use of this sensor in different environmental applications.

SUBMITTER: de Araujo DM

PROVIDER: S-EPMC8434169 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today's data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 h of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm for monitoring water quality of catchments. The aquatic drones used for data acquisition are Platypus Lutra boats. Both autonomous and manual drive is used in different parts of the navigation. The dataset is analyzed in the paper "Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework" [1] by means of recent time series clustering/segmentation techniques to extract data-driven models of the situations faced by the drones in the data acquisition campaigns. These data have strong potential for reuse in other kinds of data analysis and evaluation of machine learning methods on real-world datasets [2]. Moreover, we consider this dataset valuable also for the variety of situations faced by the drone, from which machine learning techniques can learn behavioral patterns or detect anomalous activities. We also provide manual labeling for some known states of the drones, such as, drone inside/outside the water, upstream/downstream navigation, manual/autonomous drive, and drone turning, that represent a ground truth for validation purposes. Finally, the real-world nature of the dataset makes it more challenging for machine learning methods because it contains noisy samples collected while the drone was exposed to atmospheric agents and uncertain water flow conditions.

Dataset Information

Green Composite Sensor for Monitoring Hydroxychloroquine in Different Water Matrix.

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