Mihkel Kaljurand

The rapid emergence of new illegal drugs (HHC, nitazenes, synthetic cathinones, etc.) creates a need for fast, on-site drug testing tools that can detect multiple substances in biofluids. These tools are crucial for clinicians, anti-doping experts, and law enforcement. Multiplexed portable analytical tools have a great potential to be implemented for this purposes. Moreover, such kind of instruments could be utilized in personal healthcare monitoring by enabling early-stage diagnostics of health problems. This project aims to develop electrophoresis-based analytical tools for reliable, fast and cost-effective metabolism studies in vitro, revealing the characteristics and metabolic pathways of new psychoactive substances. Additionally, new on-site biofluid testing tools (focused on oral fluid) will be developed for detecting new drugs. The results of this project will enhance drug monitoring, support public health, and improve safety.

Ukraine has a quarter of the world’s chernozems and 41.5 million hectares of agricultural land, covering 70 percent of the country. Agriculture pollutes about 60 percent of land resources and approximately 45-48 percent of reservoirs, representing 18-24 % of global GHG emissions. SmartAGRO's goal is to develop a precision fertilization solution that will help Ukraine achieve its climate policy goals. SmartAGRO is based on capillary electrophoresis technology, which enables the determination of various macro- and microelements in the soil. The goal is to develop a precise agriculture platform with recommendations for soil fertilization for agricultural crops growing in Ukraine, taking into account the chemical profile of the soil, the type and the crops grown, helping to reduce the use of fertilizers, increase/maintain yields and reduce GHG emissions.

Drug Hunter is a portable analyzer for the detection of narcotics in saliva. The project's goal was to integrate machine learning methods into an expert system for substance detection to reduce the competence required of the analyzer's user to interpret the results and thereby reduce errors in substance detection. Various machine learning methods based on supervised learning - artificial neural networks - were tested during the project. In order to prepare (annotate) the data necessary for training the neural network, a cloud service was created, where the technical parameters of all analyzes and the obtained electropherograms, on which the substances to be analyzed were marked, were stored. The best results in detecting electrophoretic patterns were obtained using a convolutional neural network (CNN). The analyzer was tested in cooperation with Estonian Police at roadside drug testing raids in Estonia.