The goal of this abstract is to present available artificial intelligence (AI) software and tools for the development, assessment, and implementation of artificial intelligence/machine learning in cardio - vascular research and clinical care, ensuring they are safe, reliable

AIM To determine whether demographic data, clinical features, and laboratory variables at disease onset can predict the response to methotrexate in juvenile idiopathic arthritis (JIA) patients. METHODS A cohort of 143 newly diagnosed JIA patients initially treated with methotrexate was enrolled in this study. Demographic, clinical, and laboratory parameters were analysed using univariate and multivariate logistic regression to identify predictors of response to methotrexate. The variables included erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), platelets, IgA, IgG, the number of active joints and age at disease onset. Treatment response was assessed at six months, with patients classified as responders (those who achieved clinically inactive disease according to the American College of Rheumatology - ACR criteria) or non-responders. RESULTS Poor response to methotrexate was associated with the number of active joints (p=0.0001; OR=2.7), baseline levels of CRP (p=0.044; OR=1.138), IgA (p=0.004; OR=2.159), and platelet count (p=0.01; OR=1.05). IgG level (P=0.236) did not correlate with the treatment response. CONCLUSION We identified widely available and clinically acceptable biomarkers that can be utilized as predictive indicators of response to methotrexate in JIA patients.

AIM To determine the prevalence of aerobic vaginitis (AV) caused by Enterococcus faecalis (E. faecalis) in human papilloma virus (HPV)-positive women with pathological Pap test and to determine the most prevalent HPV type associated with E. faecalis infection. METHODS This prospective study was conducted at the Gynaecology Centre "Dr. Mahira Jahić" Tuzla and Primary Health Care Centre Tešanj (Bosnia and Herzegovina) in the period between February 2023 and March 2024. The research included 200 women aged 25 to 50 years. The examined group consisted of 100 women with a pathological (examined group) and 100 with a normal (control group) Pap test result. RESULTS Pathological Pap smears were found in 60 (out of 100; 60 %) women in the examined group: cervical intraepithelial neoplasia (CIN) 1 and CIN 2 in two women, respectively, CIN 3 in seven, atypical squamous cells of undetermined significance (ASCUS) in 29 and atypical squamous cells-high-grade cannot be excluded (ASC-H) in two women. Overall (both groups) prevalence of E. faecalis was 25.5% (51women); in 45 (22.5%) women E. faecalis was the only bacterial isolate, of which 42 (21%) in the examined group and three (1.5%) in the control group. High-risk HPV types were found in 62 (out of 100; 62%) women with the pathological Pap smear test. The association of E. faecalis and high-risk HPV positive women was found in 35 (35%) cases (moderately positive correlation; r=0.198). CONCLUSION E. faecalis is very common in HPV 16 and 18 positive women and may represent a risk factor in the development of cervical intraepithelial lesions.

Background/Objectives: The importance of fixed-dose combinations (FDCs) for the treatment of hypertension is well established. However, from a stability perspective, FDCs present a challenge since the degradation of one active pharmaceutical ingredient (API) can be affected by the presence of another API. The aim of this study was to compare the degradation behaviors and evaluate the degradation kinetics of three antihypertensive drugs, perindopril tert-butylamine (PER), amlodipine besylate (AML), and indapamide (IND). Methods: The degradation processes were studied using the previously developed reverse phase high-performance liquid chromatographic (RP-HPLC) method after exposing each drug individually, as well as the combinations of two/three drugs, to different stress factors, such as light, oxidation, acidic, basic, or neutral pH values at different temperatures. Results: The results show that PER is most unstable under basic conditions and that AML displays a negative, while IND displays a positive effect, on PER stability when combined. AML is most affected by basic conditions and oxidation, and its stability is affected by both drugs positively; IND undergoes extreme photolysis, which is positively affected by AML but negatively by PER. Conclusions: Great care must be taken when formulating FDCs with these three drugs, as well as solutions or oral suspensions adjusted for geriatric or pediatric populations, since the stability of all three drugs is greatly affected by pH conditions, as well as light or oxidation factors and their interactions.

Background: There is no specified diagnostic procedure that can help in determining the cause of death and the diagnosis of drowning because the pathohistological signs are almost identical and non-specified. Aim: Our study aims to recognize and prove diatom appearance in organs from a forensic aspect in Bosnia and Herzegovina, and to examine which is the more specific method in the diagnosis of drowning, the diatom test or the pathohistological finding. Methods: Rats of the recommended body weight were divided into four groups: G1 (n = 8; mechanism of death—asphyxia; cause of death—suffocation, submerged 1 hour after death); G2 (n = 8: mechanism of death-asphyxia; cause of death-suffocation, immersed 72 hours after death); G3 (n = 8: mechanism of death-asphyxia; cause of death-drowning, autopsy immediately after death), and G4 (n = 8: mechanism of death-asphyxia; cause of death-drowning, post mortem 24 hours after death). Results: During the diatom analysis, four species of diatoms, Diatoma vulgaris, Melosira varians, Epithemia adnata, and Cymbella sp, were successfully recovered from the stomach. Microscopic analysis did not detect diatoms in the kidneys and brains of rats, while the pathohistological changes were relatively uniform. Conclusion: Our results propose that the diatom test is a sustainable tool for supporting the diagnosis of drowning in the forensic pathology analysis of the cause of death. This experimental study is a starting point toward the optimization of tests and sampling in cases of unexplained etiology.

Background Determining human identity has always been important in forensic investigations. Forensic dentistry has developed significantly having a key role in determining gender and age. One of the methods that is important in forensic dentistry is the analysis of orthopantomograms, which are X-rays of the complete upper and lower jaw, including the surrounding anatomical structures. The uniqueness of the dental features recorded in orthopantomograms makes them useful for individual identification, more specifically for the assessment of gender and age. This study was conducted to evaluate the application of convolutional neural networks in automating the process of gender and age estimation based on orthopantomograms, to improve accuracy and efficiency in forensic dentistry. Methodology Convolutional neural networks are powerful tools in the field of artificial intelligence for image processing and analysis because their convolutional layers extract specific features that are characteristic of a certain class. A total of 3716 orthopantomograms collected from the database of the University of Sarajevo - Faculty of Dentistry with the Dental Clinical Center were used to create convolutional neural network models for predicting gender and age. The orthopantomograms were taken in the period from January to December 2022 for the needs of doctors and providing services to patients at four polyclinics: Clinic for Dental Diseases and Endodontics, Clinic for Oral Diseases and Periodontology, Clinic for Oral Surgery, and Clinic for Pediatric and Preventive Dentistry. Results The results derived from three developed models confirm that the developed convolutional neural networks have high accuracy. The first model estimated gender, while the second and the third models estimated age within certain age ranges, the second from 12 to 24 years, and the third from 20 to 70 years. After training on the training dataset, all models achieved high accuracy on the validation dataset. The models demonstrated high accuracy without signs of overfitting, with the first model achieving 95.98%, the second model achieving 97.90%, and the third model achieving 96.12% accuracy. Conclusion This research concluded that the developed convolutional neural networks for gender and age estimation from orthopantomograms showed high accuracy. Models' predictions of gender and two age groups exceeded 95% accuracy. Therefore, convolutional neural networks can be considered useful tools for gender and age determination in forensic dentistry and can facilitate and speed up the processes of assessment and determination of essential characteristics.