: Artificial Intelligence techniques are widely used for medical purposes nowadays. One of the crucial applications is cancer detection. Due to the sensitivity of such applications, medical workers and patients interacting with the system must get a reliable, transparent, and explainable output. Therefore, this paper examines the interpretability and explainability of the Logistic Regression Model (LRM) for breast cancer detection. We analyze the accuracy and transparency of the LRM model. Additionally, we propose an NLP-based interface with a model interpretability summary and a contrastive explanation for users. Together with textual explanations, we provide a visual aid for medical practitioners to understand the decision-making process better.

Background: Endocrine-disrupting chemicals (EDCs) represent a group of chemicals which are related to the disturbances in the human hormonal system. Due to the newest research, it was discovered that their actions did not exclusively point to the hormonal system but rather to all organs of the human body. EDCs are metabolized and may excrete the influence on human metabolism. That influence can be related to the activity of different enzymes included in human metabolism. Those effects can be classified as epigenetic effects. Objective: The aim of the study was to make analysis, evaluation, examination and determination of the possible mechanisms through which EDCs may interact with different metabolically-driven diseases. Methods: This paper represents a review article that includes original and review articles that were used being published in the following databases: Medline/PubMed, ScienceDirect, Oxford Academic, and Google Scholar. Results: EDCs interact through nuclear or steroid receptors excreting their influence onto diseases such as obesity, metabolic syndrome (MetS) and non-alcoholic fatty liver disease (NAFLD). Those mechanisms are mediated through metabolic or immunological pathways. It encompasses different types of hormones, such as vistafin or inflammatory cytokines. Conclusion: It has been noticed that EDCs may influence the appearance of specifically related diseases in offspring excreting epigenetic effects. Further research must be oriented towards potential consequences and ideal pathways for prevention and treatment options.

Background: Bleeding and hematuria can be a consequence of both ESWL and URS treatment.Changes in hematological parameters may be indicative of bleeding events.Objective: The aim of the present study was to explore the hematological parameters after ESWL and ureterorenoscopy for the treatment of kidney stones. Methods: A prospective study included patients (120) with verified ureterolithiasis <10 mm in the upper half of the proximal third of the ureter. Patients were divided into two groups using the random sample method for the application of active stone removal methods ESWL or URS with contact disintegration.Patients were evaluated with routine hematological, biochemical blood parameters, and non-contrast enhanced computed abdominal tomography (CT) before the procedure.Routine laboratory analyzes were performed using standard methods and included determination of the number of erythrocytes, platelets, hemoglobin, hematocrit, glucose, INR, APTTwhich were measured preintervention, the first postoperative day and six months after the intervention. Results: The preintervention hemoglobin value in patients with urolithiasis treated with URS treatment was 140 g/L (136.2–155.7), and was statistically significantly higher compared to the measurement on the first post-intervention day [137.5 g/L (127, 2–156.7) (p<0,05)], as well as in relation to the measurement after six months [139 g/L (134.2–151.7), (p<0,05).The pre-interventional hematocrit value in patients with urolithiasis treated with URS treatment was 0.42 (0.41–0.47), but it dropped statistically significantly on the measurement on the first post-intervention day to a value of 0.41 (0.38–0, 47) (p=0.003). The hematocrit value after six months was 0.44 (0.41–0.47) and was statistically significantly higher compared to the pre-intervention measurement (p=0.002), as well as compared to the measurement on the first post-intervention day (p< 0.001). The pre-intervention INR value in patients with urolithiasis treated with URS treatment was 0.90 (0.86–1.1), and on the first post-intervention day, it increased statistically significantly to a value of 0.99 (0.89–1.1), (p=0.005).The INR value after six months continued to grow to a value of 1.02 (0.96–1.2), which was statistically significantly higher compared to the INR value measured on the first post-intervention day (p<0.001), as and in relation to the INR value measured before the intervention (p=0.007).Conclusion: The results of this study, in terms of hematological parameters, showed more favorable outcomes in patients treated with ESWL compared to URS lithotripsy. Significantly lower hemoglobin values six months after URS treatment, as well as a decrease in the number of platelets on the first postoperative day, lead to the conclusion that URS lithotripsy, which represents a more aggressive method compared to ESWL, may have less favorable consequences for patients.

: Precision agriculture is an innovative farming method that aims to maximize crop yields while minimizing waste. One of the key technologies used in precision agriculture is computer vision, which involves using cameras and sensors to collect data on crop growth and health. This data is then analyzed using machine learning algorithms to provide insights on how to optimize farming practices and improve yields. In this article, we provide an in-depth analysis of the role of computer vision in precision agriculture, with a focus on its applications in crop monitoring, the various types of cameras and sensors utilized in computer vision systems, and the diverse machine-learning algorithms employed to analyze the data collected. Through this analysis, we aim to offer a comprehensive overview of the potential of computer vision to revolutionize the way we grow and harvest crops, and the impact it could have on the future of agriculture.

Background/Aim:Dental age estimation in adults is a challenging process due to the lack of sufficient information on the accuracy of dental methods applied in the identification of persons without information on identity. Previous studies on dental age estimation in adults suggested that each method should be tested on a population other than one which was used to develop the specific method. The aim of this study was to estimate dental age in adults from Bosnia and Herzegovina with the radiographic analysis of the pulp/tooth ratio of lower canines and to determine differences from chronological age. Material and Methods: The sample for the research comprised 50 periapical radiographs, with visible intact lower canine teeth, derived from an archive of the Department of Dental Morphology, Dental Anthropology and Forensics. After the selection process, all radiographs were digitalized. Additional information used is the chronological age and sex of the person to whom the images belonged. The measurements of the pulp space and the lower canine tooth were performed using the ImageJ computer methods. The results of measurements were entered in formulas for assessments of dental age, as per equations given by the authors. Results: The research has shown that there is a significant difference in examined population between the dental age estimated by the analysis of pulp/tooth ratio of lower canine teeth and the chronological age. The standard error of regression for Cameriere model was 14.12 years, and this model proved to be statistically significant (p= 0.0017). Kvaal-Solheim model from 1994 has shown a standard error of 15.07 years, and did not prove to be statistically significant (p= 0.054), while another model from 1995, by the same authors, shows a standard error of 14.64 years, and it proved to be statistically significant (p= 0.011). Conclusions: It is possible to estimate dental age by means of pulp/tooth ratio. However, it is necessary to conduct further research that will include a larger number of examinees and different age groups and also consider the specificity of teeth in Bosnia - Herzegovina's population to acquire more accurate data on accuracy and reliability of those methods in this population.

With the expansion of renewables in the electricity markets, research on electricity storage economics is needed for a better understanding of the utilization of these systems and for improving the performance of intermittent variable generation. Collected up-to-date research of electricity storage systems published in a wide range of articles with high impact factors gives a comprehensive review of the current studies regarding all relevant parameters for storage utilization in the electricity markets. Valuable research of technical characteristics from the literature is broadened with the electricity storage analyses from an economic point-of-view. Analysis of selected technologies, considering different perspectives such as their profitability, technical maturity, and environmental aspect, is a valuable addition to the previous research on electricity storage systems. Comparing conducted analysis with the selected literature, electricity storage technologies are analyzed concerning their viability in the electricity markets. Given the current outlook of the electricity market, the main problems for storage's wider integration are still energy storage costs. These can be overcome with different applications of energy storage systems, integration of new market players, or a combination of storage technologies along with the implementation of new energy policies for storage.

The primary aim of the paper is to conduct research on the personality traits in esport players and athletes, in addition to confirming differences in personality between the two examined groups. The research has been conducted on 67 (N=67) examinees, 30 of whom are semi-professional or professional esports players who participate in state-level and regional-level competitions. The remaining 37 examinees are the highest-ranked athletes in Bosnia and Herzegovina. T-test, a type of inferential statistic, has been used to determine statistical differences in disposition between the arithmetic means of the two groups, using the BFI-44 (a=0.78) measuring instrument. It has been anticipated that esports players would be ranked lower on Extraversion (E), Agreeableness (A), and Conscientiousness (C), but higher on the Neuroticism (N) and Openness (O) dimensions compared to athletes. The research indicates that four of the five hypotheses have been confirmed - on the scale of Extraversion (E) with significance levels of p=.000 (p<0.0001); Agreeableness (A) showing p=.002 (p<0.01); Neuroticism (N) showing p=.042 (p<0.05); and Conscientiousness (C) showing significance levels of p=.004 (p<0.01) The fifth hypothesis was not confirmed on this sample. The results gathered on this sample could significantly contribute to understanding the differences between esports players and athletes.