The extent and rate of release of active substances from topical products must be sufficient to ensure their effectiveness, which depends on selecting the most appropriate formulation. This study examined allantoin emulsions and gel formulations. In water-in-oil (W/O) and oil-in-water (O/W) emulsions, the main emulsifier was varied, while the same gelling agent was used in all formulations to test the effects of oil phase presence and emulsifier type on allantoin release, as well as the formulations' rheological and textural characteristics. O/W emulsions exhibited similar release rates and the overall amount released over six hours (11-14.8%), while the highest amount of allantoin (20.9%) was released from the gel formulation. Conversely, the amount of allantoin released from the W/O emulsion (0.77%) was insufficient. Experimental data generally fit best with the Higuchi model kinetics. The formulations demonstrated shear-thinning thixotropic behavior. The greatest deviation from the Newtonian type of flow, with the smallest value of constant n (0.106-0.13) and the largest thixotropic loop area (6602.67-8140 Pas-1) were shown by O/W emulsions. The W/O emulsion exhibited the highest constant n (0.70) and smaller hysteresis area (991.23 Pas-1). Firmness and consistency values increased in the order: gel < W/O emulsion  < O/W emulsions. The O/W emulsions showed similarity in microstructure and textural characteristics, likely explaining their similar release behavior.

BACKGROUND Computational research plays an important role in predicting the chemical and physical properties of biologically active compounds important in future structural modifications to improve or modify biological activity. OBJECTIVE This research focuses on quantum chemical and spectroscopic investigations properties of synthesized 4-hydroxycoumarin derivatives. METHODS Quantum chemical calculations were obtained using B3LYP, HF, and M06-2x level methods with the 6-31++G (d,p) basis set. Afterward, IR, 1H, 13C, UV-Visible experimentally parameters were compared with the results obtained using the B3LYP/6-31+G*(d) basis set of the molecules to be able to characterize the structures. RESULTS Based on the quantum chemical calculations compound with acetamido group on the phenyl ring is the most reactive, and compound with nitro substituent is the least reactive and the the strongest electrophile among tested compounds. With the exception of compounds with dimethylamino group, all other compounds have a pronounced tautomer between between OH and C = O group. The calculated and experimental values are in agreement with each other. CONCLUSION The molecular structure in the ground state of six 3-cinnamoyl 4-hydroxycoumarin derivatives was optimized using density functional theory. The observed and computed values were compared and it can be concluded that the theoretical results were in good linear agreement with the experimental data.

BACKGROUND Preclinical drug testing requires in vitro and in vivo assessments that are vital for studying drug pharmacokinetics and toxicity. Distinct factors that play an important role in drug screening, such as hydrophobicity, solubility of the substance and serum protein binding can be challenging by inducing result inconsistencies. Hence, establishing accurate methods to quantify drug concentrations in cell cultures becomes pivotal for reliable and reproducible results important for in vivo dosing predictions. OBJECTIVE This research focuses on developing an optimized analytical approach via high-pressure liquid chromatography (HPLC) to determine thymoquinone (TQ) levels in monolayer cell cultures. METHODS The method's validation adheres to the International Council for Harmonisation (ICH) guideline M10, ensuring its acceptance and applicability. Using an HPLC system with a Diode Array Detector (DAD), the study fine-tuned various parameters to achieve an efficient separation of TQ. Validation covered specificity, sensitivity, matrix effects, linearity, precision, and accuracy, alongside assessing TQ stability in RPMI-1640 medium. RESULTS The HPLC method exhibited remarkable TQ specificity, free from interfering peaks at the analyte retention. Sensitivity analysis at the lower limit of quantification (LLOQ) revealed 5.68% %CV and 98.37% % mean accuracy. Matrix effect evaluation showcased accuracy within 85-115%. Linearity spanned in the concentration range of 2-10 μM with a correlation coefficient (r2) of 0.9993. Precision and accuracy were aligned with acceptance criteria. The proposed method was found to be greener in terms of usage of persistent, bioaccumulative, and toxic chemicals and solvents, corrosive samples, and waste production. CONCLUSION The developed HPLC-DAD method emerges as specific, accurate, sensitive, and reliable for TQ determination in cell cultures. It ensures robust TQ quantification, enhancing precise in vitro assessments and dependable dosing predictions for in vivo studies. Further research is advocated to investigate TQ's stability across diverse environmental conditions.

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.

Celiac disease is defined as an autoimmune disorder that is the result of a reaction of adaptive immune response of sensitive individuals, to gluten-containing cereals. The aim of this study was to assess the nutritional habits of patients with the diagnosis and people without the diagnosis, to evaluate the importance of adhering to a strict gluten-free diet, recognizing properly declared gluten-free products and to assess the quality of life of people diagnosed with celiac disease. Anthropometric and dietary tests were conducted through a survey by a total of 96 people, 56 respondents were diagnosed with celiac disease, while the remaining 40 respondents were undiagnosed, and of whom 32 were on a gluten-free diet. The majority of respondents (58.92%) have an adequate level of nutrition; however, 30.37% of respondents are overweight, including the degree of obesity in four cases. The most consumed cereals are rice, corn and white ready-made gluten-free flour mixtures, while the respondents consume the least pseudo-cereals, whole grains and micro-cereals. Within the survey CD-QOL (Celiac Disease Quality of Life) we assessed the quality of life, general the attitude of patients according to the given claims related to celiac disease. The obtained results led us to the conclusion that the dietary habits of celiac patients are somewhat worse than those of the undiagnosed group, although these are very small differences. It has been found that there is some confusion among people diagnosed regarding properly declared and certified gluten-free products, while most respondents believe that a strict gluten-free diet is the only effective treatment for the disease.

A 16-year-old girl with an asymptomatic large splenic cyst. The disease was discovered by an accidental ultrasound of the abdomen due to a urinary infection. The patient was followed up conservatively for 3 years, and after the gradual enlargement of the cyst, laparoscopic surgery was indicated. The operation and postoperative follow-up for a period of 6 months is satisfactory.

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.

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 %.

Pulmonary emphysema is a complicated disease caused by irreversible damage to the wall of the pulmonary alveoli and causes 5% of the total mortality worldwide. This paper presents the development of an artificial neural network (ANN) for the diagnosis of pulmonary emphysema. Following biomarkers were used for the development of the ANN: AAT (alphal-antitrypsin), FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity) and FEV1/FVC (ratio forced expiratory volume in 1 second / forced vital capacity). The dataset consisted of 300 patients: 210 healthy subjects and 90 subject with disease. The neural network has 4 input parameters and 1 output parameter. For the final architecture, a neural network with 13 neurons in hidden layer was chosen based on the training results. The developed ANN has shown good performance and has a potential for use in this field.

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.