Solvent and substitution effects on the UV/Vis spectroscopic and fluorescence behaviour of seven synthesized 3-substituted 4-hydroxycoumarin derivatives were tested. The tested compounds were dissolved in ethyl acetate, acetonitrile, and dimethyl sulfoxide. Absorption and emission spectra were recorded in the range of 200–800 nm. All tested 4-hydroxycoumarin derivatives showed good absorption in a wide range of 200–550 nm, depending on the properties of the substituents on the benzene ring of the cinnamoyl moiety and the type of solvent. In comparison to the unsubstituted analogue, compounds with an electron-donating group exhibited bathochromically shifted UV/Vis absorption and emission spectra. The highest fluorescence quantum yield was observed for compounds with dimethylamino and acetamido groups as substituents at the benzene ring. Considering that both substitution and solvent affect the absorption and emission spectra of the tested compounds, it can be concluded that judiciously selecting these parameters can improve their absorption and fluorescence properties, making them suitable for various analytical uses.

Skin sensitization is a crucial endpoint in the safety assessment of chemicals, with the Direct Peptide Reactivity Assay (DPRA) emerging as a valuable in chemico method for evaluating a substance's sensitization potential. This review delves into the principles, applicability, and limitations of the DPRA within the context of the Adverse Outcome Pathway (AOP) framework for skin sensitization. We examine the DPRA'srole in addressing the molecular initiating event of skin sensitization, its integration into Integrated Approaches to Testing and Assessment (IATA), and its performance in predicting sensitizers. The review also highlights the challenges in testing certain categories of chemicals and the importance of considering the DPRA's results alongside other complementary methods. By providing a comprehensive overview of the DPRA, this review aims to inform researchers, regulators, and clinicians about its utility and limitations in the context of skin sensitization testing.

Consumption of fish has increased in last 50 years. Fish as a food is changing red meat because it has unsaturated fat and it is the best source of omega 3 fatty acids. Beside it is full of minerals, vitamins and it has high biological value of proteins.The content of heavy metals in the muscle tissue of fish is directly related to the pollution of the water they come from The analysis of the content of heavy metals was done by the Institute of Public Health of the Federation of Bosnia and Herzegovina.The content of lead (Pb) in the tested samples of fresh fish ranged from 0.0015 to 0.0381 mg/kg. The measured content of cadmium (Cd) in the examined samples was in the range of 3.3*10-5 to 0.0053 mg/kg. The content of arsenic (As) in the tested samples ranged from 0.0085 to 1.1668 mg/kg. The mercury (Hg) content in the tested samples of fresh fish ranged from 0.0033 to 0.0991 mg/kg, which is within the allowed values prescribed by the Rulebook. It has been statistically proven that there is a significant difference in the measured values of lead, arsenic and cadmium in the samples of sea and freshwater fish. Aim of this work was to establish do the samples of fresh fish contain concentration of heavy metals more than concentrations prescribed in Rule book about allowed amounts of certain contaminants in food. Thereby ten samples of fresh fish were tested, five samples of marine fish and five samples of freshwater fish. Results showed that all samples of fish satisfy allowed concentration of heavy metals according to the Rule book.

Objective. The aim of this study was to investigate students’ knowledge, attitudes and hesitancy regarding COVID-19 vaccination. Methods. A cross-sectional questionnaire-based survey was conducted among a total of 1282 medical students and 509 non-medical students at four public universities in Bosnia and Herzegovina: Tuzla, Sarajevo, Banja Luka, and Mostar. Results. A significantly higher rate of vaccination was observed in the group of medical students as well as a higher level of knowledge about vaccination in general and vaccines against the COVID-19 disease. Students who received the COVID-19 vaccine had a higher level of knowledge about vaccination in general and COVID-19 vaccines in particular compared to the non-vaccinated students in the medical and non-medical groups, respectively. Furthermore, vaccinated students, regardless of the course they are taking, showed generally stronger positive attitudes compared to non-vaccinated students, regarding the safety and effectiveness of the COVID-19 vaccine. Both groups of students believe that the rapid development of the vaccine is contributing to refusal or hesitancy to receive a vaccine against COVID-19. Social media/networks were the main sources of information about the COVID-19 vaccine. We did not find any contribution of social media to the reduced level of COVID-19 vaccine coverage. Conclusion. Education of students about the benefits of the COVID-19 vaccine will lead to its better acceptance as well as the development of more positive attitudes towards vaccination in general, especially having in mind that students are the future population of parents, who will make decisions about vaccinating their children.

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus. Diagnosis of the disease itself is difficult because the incubation period is long, often the disease is initially without some characteristic symptoms, but also due to a lack of laboratory methods. Artificial intelligence is increasingly being used nowadays to make it easier and faster to assess the illness. As hepatitis C is a rising healthcare burden it is of utmost importance to construct effective and reliable screening methods. As AI has already proven useful for diagnosis of a variety of conditions based on clinical parameters, this study focuses on the application of artificial neural network (ANN) for hepatitis C diagnosis. In this study, a database of 1000 respondents divided into two groups was used to develop the ANN: healthy (n = 200) and sick (n = 800). Monitoring parameters were: albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, bilirubin, acetylcholinesterase and anti-HCV antibodies. The overall accuracy of the developed ANN was 97,78%, which indicates that the potential of artificial intelligence in diagnosing hepatitis C is enormous, and in the future, attention should be paid to the development of new systems with as much data as possible.