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.

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.

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.

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.

The virus SARS-Co V -2 that has caused a pandemic of COVID-19 in 2019 is still a major concern for health care systems. The reason for this is the fact that the outcome of the disease is difficult to predict, as deadly complications can occur in all people. Diagnosing COVID-19 relies on polymerase chain reaction (PCR) testing and antigen testing, both of which require special referral. The aim of this study was to develop artificial intelligence (AI) expert system which will facilitate COVID-19 diagnosis based on parameters that can be readily collected from blood specimens. The database contains 1000 samples, divided into 2 categories: (1) healthy and (2) sick subjects The following parameters were used: CRP, LDH, SE, AST, ALT, D-dimer and IL-6. The sensitivity of the developed system was 100%, specificity 98.33%, and accuracy 99.67%, on the basis of which we can conclude that the use of AI in the diagnosis of COVID19 has a significant potential.

This paper focuses on the problem of diagnosing polycystic ovary syndrome (PCOS), which is one of the leading disorders of the female endocrine system. Although the incidence of this syndrome is quite high, physicians and patients still often encounter problems in their detection, as well as with the ineffectiveness of prescribed therapy. For the development of expert system, a database containing following parameters was used: oligo ovulation, anovulation, free testosterone, free androgen index (FAI), calculated bioavailable testosterone, androstendione, dehydroepiandrosterone, ovarian volume, number of follicles, obesity. The presented dataset contains 1000 samples distributed in two categories: (1) heatlhy subjects and (2) subjects with disease. The purpose of the developed system is to classify instances with polycystic ovary syndrome using artificial neural networks (ANN s). The overall performance evaluation of the system resulted with accuracy of96.1 %, sensitivity of96.8% and specificity of90% indicating significant potential of ANNs in this field. Since the system predicted a total of 157 positive and 23 negative, this leads us to the result that the sensitivity of our system is 96.8%, specificity 90% and accuracy 96.1 %.

Diagnosis of anemia is a time intensive and medically expensive procedure requiring a multitude of tests to establish a final diagnosis. Classification is an even more complex procedure that often takes years to complete thus delaying proper treatment and worsening the prognosis. This paper presents the application of machine learning, K-nearest neighbors (KNN), in order to diagnose and classify anemia. Monitoring parameters used as input for diagnosis were: age, sex, ferritin, transferrin, vitamin B12, erythrocyte count, iron, folic acid, hemoglobin, while the parameter relevant for classification was MCV. The results of the study indicate significant possibilities for the application of this system in the field of medical diagnostics.

Plant-derived products are frequently found as ingredients in cosmetics. However, the current data show non-neglectable skin sensitizing potential of these preparations suggesting an urgent need for data regarding their health safety profile. The aim of this study was to assess the skin sensitization potential of commercial essential oils by selected Lamiaceae species (Lavandula angustifolia, Melissa officinalis, Mentha longifolia, Thymus vulgaris, Salvia officinalis, and Rosmarinus officinalis) using a chemistry-based Direct Peptide Reactivity Assay (DPRA) in order to predict their potential allergic properties. In the DPRA assay, nucleophile-containing synthetic peptides (cysteine peptide and lysine peptide) were incubated with the test substance for 24 h. Depletion of the peptide in the reaction mixture was measured by high-pressure liquid chromatography (HPLC) using UV detection and the average peptide depletion data for cysteine and lysine was then calculated. Menthae longifoliae aetheroleum showed no or minimal reactivity with 4.48% cysteine depletion, Rosmarini aetheroleum and Salviae aetheroleum showed low reactivity with the 12.79% and 15.34% of cysteine depletion, respectively, while the other analyzed essential oils showed moderate reactivity with the cysteine depletion between 23.21 and 48.43%. According to DPRA predictive analysis, only Menthae longifoliae aetheroleum can be classified as negative, while all other essential oils may be classified as positive, thus having the potential to cause skin sensitization.