Background Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) had been in force for 25 years, in 2017 the new MD Regulation (MDR) was introduced. One of the more stringent requirement is a need for better control of MD safety and performance post-market surveillance mechanisms. Objective To address this, we have developed an automated system for management of MDs, based on their safety and performance measurement parameters, that use machine learning algorithm as a core of its functioning. Methods In total, 1997 samples were collected during the inspection process of defibrillator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. This paper presents solution developed for defibrillators, but proposed system is scalable to any other type of MDs, both diagnostic and therapeutic. Results Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR). In addition, random forest regressor and XG Boost algorithms were tested for their predictive capabilities in the field of defibrillator output error prediction. These algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The highest accuracy achieved on this dataset was 94.8% using the Naive Bayes algorithm. The XGBoost Regressor with its r2 of 0.99 emerged as a powerful tool, showcasing exceptional predictive accuracy and the ability to capture a substantial portion of the dataset's variability. Conclusion The results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automatization of defibrillator maintenance scheduling in terms of increased safety and treatment of patients, on one side, and cost optimization in MD management departments, on the other side.

Neurological impairment disorders in fetuses, such as cerebral palsy, epilepsy, and autism spectrum disorder, can arise from numerous factors impacting the development of the fetal nervous system. Although diagnosing these disorders early is difficult, it is essential for prompt intervention. Recent progress in deep learning and ultrasound technology offers the potential to create a tool for early detection. Development of the TRUEAID system is based on combining the meticulously tuned Kurjak Antenatal Neurodevelopmental Test (KANET) with a sophisticated convolutional neural network for construction of an AI empowered ultrasound module capable of automated diagnostic decision support in the field of fetal neurodevelopmental risk assessment. The model's performance was evaluated using accuracy metrics, precision, sensitivity, specificity, F1 score, and Mathesson Correlation Coefficient (MCC). The custom CNN architecture achieved an overall accuracy of 93.83%. This pilot study lays the foundation for AI-based fetal neurobehavioral assessment, providing a promising tool for the early detection of fetal neurological impairment disorders. The research holds implications for improving outcomes for affected children and making advanced diagnostic capabilities accessible in diverse healthcare settings.

BACKGROUND: Left atrial strain (LAS) analysis represents a newer non-invasive, sensitive and specific technique for assessing left atrial (LA) function and early detection of its deformation and dysfunction. However, its applicability in mitral regurgitation (MR) in pediatric population remains unexplored, raising pertinent questions regarding its potential role in evaluating the severity and progression of the disease. OBJECTIVE: To investigate the impact of chronic MR in children and adolescents on LA remodeling and function. METHODS: The study included 100 participants. Patients with primary and secondary chronic MR lasting at least 5 years fit our inclusion criteria. The exclusion criteria from the study were: patients with functional mitral regurgitation due to primary cardiomyopathies, patients with artificial mitral valve, patients with MR who had previously undergone surgery due to obstructive lesions of the left heart (aortic stenosis, coarctation of the aorta), patients with significant atrial rhythm disorders (atrial fibrillation, atrial flutter). The echocardiographic recordings were conducted by two different cardiologists. Outcome data was reported as mean and standard deviation (SD) or median and interquartile range (Q1–Q3). RESULTS: The study included 100 participants, of whom 50 had MR and the remaining 50 were without MR. The average age of all participants was 15.8 ± 1.2 years, with a gender distribution of 37 males and 63 females. There was a significant difference in the values of LA volume index (LAVI), which were higher in patients with MR ( p = 0.0001), S/D ratio (and parameters S and D; p = 0.001, p = 0.0001, p = 0.013), mitral annulus radius ( p = 0.0001), E/A ratio ( p = 0.0001), as well as septal e’ (m/s), lateral e’ (m/s), and average E/e’ ratio, along with the values of TV peak gradient and LV global longitudinal strain (%). There was no significant difference in LA strain parameters, nor in LA stiffness index (LASI). CONCLUSION: Our findings revealed significant differences in several echocardiographic parameters in pediatric patients with MR relative to those without MR, providing insight into the multifaceted cardiac structural and functional effects of MR in this vulnerable population.

BACKGROUND: Left atrial stiffness index (LASI), defined as the ratio of early diastolic transmitral flow velocity/lateral mitral annulus myocardial velocity (E/e’) to peak atrial strain, reflects reduced left atrial (LA) compliance and represents an emerging marker that can be used for noninvasive measurement of fibrosis of LA in patients with mitral regurgitation (MR). OBJECTIVE: To investigate the impact of chronic MR in children and adolescents on the remodeling and function of the LA, quantified through strain parameters and diastolic function. METHODS: The study included fifty patients ( n = 50) diagnosed with primary and secondary chronic MR lasting at least 5 years. The echocardiographic recordings were performed by a third party, two cardiologists actively engaged in echocardiography on a daily basis. RESULTS: Older participants had higher values of the LASI ( r = 0.467, p = 0.001). Participants with higher LASI values had a smaller LA reservoir ( r = 0.784, p = 0.0001) and smaller LA conduit values ( r = - 0.374, p = 0.00). Participants with higher LASI values had a larger LA diameter ( r = 0.444, p -value= 0.001) and higher average E/e’ ratio ( r = 0.718, p = 0.0001). There was a significant difference ( p = 0.04) in the LASI among participants based on the MR jet area ( < 20.85% ⩾ 20.85%), LASI was higher in participants with an area greater than 20.85%. Differences in other parameters such as LA reservoir, LA conduit, LA contractile were not statistically significant. CONCLUSION: Increased LA stiffness is associated with diminished atrial compliance and reservoir capacity, and LASI has a potential to as an early marker for assessing disease severity and progression in pediatric MR.

In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms. Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes. The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes. The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

Statisticians rank oral and lip cancer sixth in global mortality at 10.2%. Mouth opening and swallowing are challenging. Hence, most oral cancer patients only report later stages. They worry about surviving cancer and receiving therapy. Oral cancer severely affects QOL. QOL is affected by risk factors, disease site, and treatment. Using oral cancer patient questionnaires, we use light gradient Boost Tree classifiers to predict life quality. DIAS records were used for 111 oral cancer patients. The European Organisation for Research and Treatment of Cancer’s QLQ-C30 and QLQ-HN43 were used to document the findings. Anyone could enroll, regardless of gender or age. The IHEC/SDC/PhD/OPATH-1954/19/TH-001 Institutional Ethical Clearance Committee approved this work. After informed consent, patients received the EORTC QLQ-C30 and QLQ-HN43 questionnaires. Surveys were in Tamil and English. Overall, QOL ratings covered several domains. We obtained patient demographics, case history, and therapy information from our DIAS (Dental Information Archival Software). Enrolled patients were monitored for at least a year. After one year, the EORTC questionnaire was retaken, and scores were recorded. This prospective analytical exploratory study at Saveetha Dental College, Chennai, India, examined QOL at diagnosis and at least 12 months after primary therapy in patients with histopathologically diagnosed oral malignancies. We measured oral cancer patients’ quality of life using data preprocessing, feature selection, and model construction. A confusion matrix was created using light gradient boosting to measure accuracy. Light gradient boosting predicted cancer patients’ quality of life with 96% accuracy and 0.20 log loss. Oral surgeons and oncologists can improve planning and therapy with this prediction model.

BACKGROUND: Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE: The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS: A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms “artificial intelligence in obstetrics”, “maternal-fetal medicine”, and “machine learning” combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS: According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION: The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

BACKGROUND: Left atrial (LA) strain analysis has emerged as a noninvasive technique for assessing LA function and early detection of myocardial deformation. Recently, its application has also shown promise in the pediatric population, spanning diverse cardiac conditions that demand accurate and sensitive diagnostic measures. OBJECTIVE: This research article endeavors to explore the role of LA strain parameters and contribute to the growing body of knowledge in pediatric cardiology, paving the way for more effective and tailored approaches to patient care. METHODS: A comprehensive literature review was conducted to gather evidence from studies using echocardiographic strain imaging techniques across pediatric populations. RESULTS: LA strain parameters exhibited greater sensitivity than conventional atrial function indicators, with early detection of diastolic dysfunction and LA remodeling in pediatric cardiomyopathy, children with multisystem inflammatory syndrome, rheumatic heart disease, as well as childhood renal insufficiency and obesity offering prognostic relevance as potential markers in these pediatric subpopulations. However, there remains a paucity of evidence concerning pediatric mitral valve pathology, justifying further exploration. CONCLUSION: LA strain analysis carries crucial clinical and prognostic implications in pediatric cardiac conditions, with reliable accuracy and sensitivity to early functional changes.

Partial or complete dentures are constructed from thermoplastic resins that are thermally processed and molded. This review examines the presently available evidence for the cytotoxicity of thermoplasticized denture base resins on human gingival epithelial cells, adipose cells, and fibroblasts; human amnion fibroblasts; and mouse fibroblasts. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar databases to identify relevant articles to be included in the review until September 2022. Clinical, in vivo, and in vitro studies in English language were searched for. The quality of the studies was assessed using the Toxicological data Reliability Assessment tool (ToxRTool) developed by the European Commission’s Joint Research Centre. GRADE assessment was used to evaluate the certainty of evidence. Seven in vitro studies were included in the review. The overall risk of bias was determined to be high, with the majority of studies assessed found to be reliable with restrictions or not reliable. Only two studies were considered reliable without restrictions based on ToxRTool assessment. The effect of thermoplastic denture base resins on viability and cell adherence of human gingival or amnion fibroblasts and mouse fibroblasts (L929s) is not significant. Conditioned media from unpolished specimens of resins were significantly more toxic to cultured cells than those from polished specimens. This may be of concern in cases of poor post-processing of dentures. Based on the limited evidence available, there is low-certainty evidence that thermoplastic denture base resins appear to be biocompatible and show insignificant cytotoxicity. Further well-designed trials adhering to standard reporting guidelines and using objective measures are necessary before outlining universal guidelines for best practice. Long-term in vivo and clinical assessment is necessary to corroborate laboratory findings with clinical outcomes. Denture base resins are in constant contact with oral tissues, and cytotoxic components released by the resins may irritate or inflame the tissues or provoke an allergic response.

In order to raise and harmonize the quality standards of pharmaceutical studies at the national level of Bosnia and Herzegovina and thus get closer to the implementation and quality assurance of study programs of EU countries, a team of professors from the University of Sarajevo-Faculty of Pharmacy prepared and was awarded the Erasmus+ project IQPharm. IQPharm (Innovating quality assessment tools for pharmacy studies in Bosnia and Herzegovina) aims at capacity building of quality management, and aims to introduce new tools for quality improvement, digitization and modernization of pharmacy studies at public universities in Bosnia and Herzegovina, including strengthening semi-structured experiential education in line with EU standards and higher education regulations for regulated professions. The introduction of new tools for the assessment of the quality of study programs (KREF) enables the development of evidence-based recommendations for change, modification and innovation of existing methods of knowledge transfer, didactic approaches and curricula. The introduction of a new system of proficiency testing through experiential education (OSCE) sets equal standards at the national level for the learning outcomes of graduate pharmacists. The development of E-platform ensures the digitization and modernization of experiential education management. Experiential education at the level of Bosnia and Herzegovina will be significantly improved through the introduction of the E-platform, by raising the standards of the practice itself and facilitating its implementation by student services, students and their mentors. A special part of this project is the development of free modules, which are extracurricular subjects intended to enrich the knowledge of students and graduates of pharmacy, They should track the labor market trends, and thus make higher education more agile and attractive.

Introduction: Heart failure (HF) still remains as one of the most common causes of hospital admission with a high mortality rate. Aim: To investigate the possible prognostic role of brain natriuretic peptide (BNP), high-sensitivity (hs) cardiac troponin (cTn) I, cystatin C, and cancer antigen 125 (CA125) in the prediction of decompensation after an index hospitalization and to investigate their possible additive prognostic value. Patients and Methods: Two hundred twenty-two patients hospitalized with acute HF were monitored and followed for 18 months. Results: BNP at discharge has the highest sensitivity and specificity in the prediction of decompensation. For a cutoff value of 423.3 pg/ml, sensitivity was 64.3% and specificity was 64.5%, with a positive predictive value of 71.6% and an area under the curve (AUC) of 0.69 (P < 0.001). The hazard risk (HR) for decompensation when the discharge BNP was above the cutoff value was 2.18. Cystatin C, at a cutoff value of 1.46 mg/L, had a sensitivity of 57% and specificity of 57.8%, with a positive predictive value of 65.8% and an AUC of 0.59 (P = 0.028). CA125, in the prediction of decompensation in patients with acute heart failure (AHF) and at a cutoff value of 80.5 IU/L, had a sensitivity of 60.5% and specificity of 53.3%, with a positive predictive value of 64.5% and an AUC of 0.59 (P = 0.022). The time till onset of decompensation was significantly shorter in patients with four versus three elevated biomarkers (P = 0.047), with five versus three elevated biomarkers (P = 0.026), and in patients with four versus two elevated biomarkers (P = 0.026). The HR for decompensation in patients with five positive biomarkers was 3.7 (P = 0.001) and in patients with four positive biomarkers was 2.5 (P = 0.014), compared to patients who had fewer positive biomarkers. Conclusion: BNP, cystatin C, and CA125 are predictors of decompensation, and their combined usage leads to better prediction of new decompensation.

The inflammatory injury of the mucous membranes lining the digestive tract, from the mouth to the anus, is called mucositis. One of the intriguing and compelling new therapeutic modalities that has emerged in recent decades due to advances in our understanding of this condition’s pathophysiology is probiotics. The purpose of this meta-analysis is to evaluate the efficiency of probiotics in the treatment of chemotherapy-induced mucositis for head and neck malignancies; a literature search was performed on PubMed, Lilacs, and Web of Science, and articles published from 2000 to 31 January 2023 were considered, according to the keywords entered. The term “Probiotics” was combined with “oral mucositis” using the Boolean connector AND; at the end of the research, 189 studies were identified from the search on the three engines. Only three were used to draw up the present systematic study and metanalysis; this meta-analysis showed that the treatment of mucositis with probiotics is an effective method, and the analysis of the results of these studies showed that the use of probiotics promoted a decrease in the severity of mucositis symptoms.