Molecules

Synthetic cathinones are cathinone analogues that humans have artificially created. The first compounds appeared on the European market in 2005. They belong to a class of drugs called stimulants, classified as new psychoactive substances. Synthetic cathinones are very popular; people use these drugs because they are cheaper “substitutes” for other stimulants. They produce psychostimulant and hallucinogenic effects similar to cocaine, amphetamine, and MDMA, among others. Despite their presence on the market for several years, the precise toxicological impacts of these compounds on the human body remain unknown. Studies were conducted on the effects of selected cathinones (mephedrone, clephedrone) on blood cells: erythrocytes and platelets. The effect of cathinones was determined by measuring the surface density of biological membranes using microelectrophoresis. The continued popularity of these compounds, coupled with limited knowledge of their precise effects on the human body, makes the problem significant and requires ongoing research. Based on the results obtained for mephedrone and clephedrone, it can be concluded that at the tested concentrations (170 ng/mL and 2700 ng/mL), they alter the surface charge density of the biological membranes of red blood cells and platelets.

In the present study, ten type-V natural deep eutectic solvents (NADESs) were synthesized and comprehensively characterized, based on urea as a hydrogen-bond acceptor and three different groups of donors—glycerol, organic carboxylic acids, and carbohydrates. Their physicochemical parameters, spectral characteristics (FTIR), surface tension, and solvatochromic properties were determined using Nile Red, betaine 30, and Kamlet–Taft parameters. The densities of the systems (1.243–1.361 g/cm³) and the high values of molar refraction and polarizability indicate the formation of highly organized hydrogen-bonded networks, with the incorporated carboxyl and hydroxyl groups enhancing the structural compactness of the NADES. Surface tension varied significantly (46.9–80.3 mN/m), defining systems with low, medium, and high polarity. Solvatochromic analysis revealed high E_NR, E_T(30), and E_T^N values, positioning all NADES as highly polar media, comparable or close to water, but with distinguishable H-bond donating/accepting ability depending on the third component. The normalized Kamlet–Taft parameters show that the NADES cover a broad solvent spectrum—from highly H-bond accepting to strongly H-bond donating or dipolar systems—highlighting the potential for fine-tuning the solvent according to target applications. The obtained results highlight the applicability of these NADESs as green, tunable media for the extraction and solvation of bioactive compounds.

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets.