Background Introduction: Vitamin D plays significant role in calcium metabolism and in bone and vascular calcifications. Objective: To investigate the association between vitamin D level, arterial hypertension, arterial stiffness and coronary calcifications detected by MSCT. Method: A 2 female case report comparative to each other investigated the correlation between vitamin D serum level, blood pressure, arterial stiffness and severity of the coronary calcification using MSCT diagnostic tool estimating the calcium score. Results: The first case report showed that decreased level of vitamin D is correlated with increased blood pressure, increased arterial stiffness and with a severe coronary calcifications. The second case report showed normal blood pressure, normal vascular age and low calcium score in a no-defficient vitamin D female. Conclusion: Vitamin D has impact on blood pressure, arterial stiffness, coronary calcifications and coronary heart disease. The lower vitamin D, the higher arterial blood pressure, arterial stiffness and coronary calcium score.

CONTEXT Immune-oncology strategies are revolutionising the perioperative treatment in several tumour types. The perioperative setting of renal cell carcinoma (RCC) is an evolving field, and the advent of immunotherapy is producing significant advances. OBJECTIVE To critically review the potential pros and cons of adjuvant and neoadjuvant immune-based therapeutic strategies in RCC, and to provide insights for future research in this field. EVIDENCE ACQUISITION We performed a collaborative narrative review of the existing literature. EVIDENCE SYNTHESIS Adjuvant immunotherapy with pembrolizumab is a new standard of care for patients at a higher risk of recurrence after nephrectomy, demonstrating a disease-free survival and overall survival benefit in the phase 3 KEYNOTE-564 trial. Current data do not support neoadjuvant therapy use outside clinical trials. While both adjuvant and neoadjuvant immune-based approaches are driven by robust biological rationale, neoadjuvant immunotherapy may enable a stronger and more durable antitumour immune response. If neoadjuvant single-agent immune checkpoint inhibitors demonstrated limited activity on the primary tumour, immune-based combinations may show increased activity. Overtreatment and a risk of relevant toxicity for patients who are cured by surgery alone are common concerns for both neoadjuvant and adjuvant strategies. Biomarkers helping patient selection and treatment deintensification are lacking in RCC. No results from randomised trials comparing neoadjuvant or perioperative immune-based therapy with adjuvant immunotherapy are available. CONCLUSIONS Adjuvant immunotherapy is a new standard of care in RCC. Both neoadjuvant and adjuvant immunotherapy strategies have potential advantages and disadvantages. Optimising perioperative treatment strategies is nuanced, with the role of neoadjuvant immune-based therapies yet to be defined. Given strong biological rationale for a pre/perioperative approach, there is a need for prospective clinical trials to determine clinical efficacy. Research investigating biomarkers aiding patient selection and treatment deintensification strategies is needed. PATIENT SUMMARY Immunotherapy is transforming the treatment of kidney cancer. In this review, we looked at the studies investigating immunotherapy strategies before and/or after surgery for patients with kidney cancer to assess potential pros and cons. We concluded that both neoadjuvant and adjuvant immunotherapy strategies may have potential advantages and disadvantages. While immunotherapy administered after surgery is already a standard of care, immunotherapy before surgery should be better investigated in future studies. Future trials should also focus on the selection of patients in order to spare toxicity for patients who will be cured by surgery alone.

In recent advancements in robotics, Artificial Intelligence (AI) methods such as Deep Learning, Deep Reinforcement Learning (DRL), Transformers, and Large Language Models (LLMs) have significantly enhanced robotic capabilities. Key AI models driving advancements in robotic vision include Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), the DEtection Transformers (DETR), the YOLO family of algorithms, segmentation techniques, and 3D vision technologies. Deep Reinforcement Learning (DRL), an AI technique where agents learn optimal behaviors through trial and error interactions with their environment, enables robots to perform complex tasks autonomously. Transformers, originally developed for natural language processing, have been adapted to robotics for tasks involving sequence prediction and data understanding, improving perception and decision-making processes. LLMs leverage vast amounts of text data to enhance robot-human interaction, enabling robots to understand and generate human-like language, thus improving their communicative and collaborative abilities in various applications. The integration of these AI methods enhances the adaptability, efficiency, and overall performance of robotic systems, paving the way for more sophisticated and intelligent autonomous agents.

In the last ten years, the development and research of advanced technologies, as well as their application in all segments of society, have led to major changes and reshaping of the new world. New innovations are occurring on a daily basis, but their application is not going fast enough due to the rigid infrastructure. However, in order to secure an optimal future, we all have to adapt to the changes that are coming. The developed countries have adopted the strict implementation of advanced technologies of Industry 4.0, some of which include: Internet of Things (IoT), Big Data, Cloud Computing, smart sensors, Radio Frequency Identification (RFID), 3D printing, advanced security systems, Virtual and Augmented Reality (VAR), etc. Robotics is the basic and first technology that has been implemented since the 60s of the last century, with artificial intelligence coming in the spotlight in the last ten years. Artificial intelligence is becoming a key to the development of advanced robots, as it enables them to adapt to unpredictable situations, to learn from experience and make intelligent decisions.Robots use AI to process sensor data, navigate, recognize objects, plan paths and interact with the environment. In short, artificial intelligence enables robots to be smart, whereas robotics uses AI to create autonomous and useful devices. This symbiosis contributes to progress in many industries, including healthcare, manufacturing and transportation. Artificial intelligence (AI) and robotics are two key fields that complement each other. The paper presents the trend of applied and approved patents in artificial intelligence and robotics, as well as an example of the use of artificial intelligence in advanced robots to perform certain tasks. Artificial intelligence (AI) is having an increasing impact on robotics, opening up many possibilities.

U radu je predstavljen postupak izbora najpogodnijeg numeričkog modela za utvrđivanje indeksa staništa (SI – site index) kao apsolutne mjere proizvodnog potencijala (boniteta) staništa jednodobnih nenjegovanih sastojina bijelog bora na karbonatnim supstratima u BiH. Objekat istraživanja su predstavljale jednodobne nenjegovane sastojine bijelog bora različitih taksacionih i stanišnih karakteristika. Metodom privremenih oglednih parcela prikupljeno je više općih i taksacionih podataka, a zatim su njihovom obradom i analizom utvrđeni najvažniji strukturni i proizvodni parametri sastojina odvojeno po relativnim visinskim bonitetnim klasama staništa (RB). Za utvrđivanje numeričkog modela za procjenu indeksa staništa (SI) primijenjene su metode korelacione i regresione analize, a za predstavljanje veličina osnovnih taksacionih elemenata prema veličinama SI grafička metoda. U cilju predstavljanja veličina osnovnih taksacionih elemenata po utvrđenim SI klasama uspostavljena je korelaciona veza između SI50 (pri starosti od 50 godina)i postojećih relativnih bonitetnih klasa (RB) jednodobnih sastojina bijelog bora. Ova veza je poslužila za izradu proizvodne diferencijacije staništa jednodobnih sastojina bijelog bora na karbonatnim susptratima u BiH koja omogućava prikaz veličina osnovnih taksacionih elemenata ovih sastojina zavisno od starosti i SI50. Poređenjem utvrđenih rezultata istraživanja s odgovarajućim rezultatima drugih autora zaključeno je da su jednodobne sastojine bijelog bora u BiH srednje produktivne.

Objective The objective of this study was to evaluate the root canal morphology of third molars in the Bosnia-Herzegovina population. Materials and methods A total of 241 extracted third molars (105 maxillary and 136 mandibular) were subjected to a clearing procedure. The specimens were categorized into ten groups based on the Alavi classification for maxillary third molars (MaxTMs), and six groups were based on the Gulabivala classification for mandibular third molars (ManTMs). Root canal type according to the Vertucci classification, the presence and position of lateral canals, and intercanal communication were analyzed using a stereomicroscope x15. Results MaxTMs had three roots in 77.13% of the samples. Among MaxTMs, the most common morphology was three fused roots (33.33%) and Vertucci’s type VIII (54. 28% of samples in Alavi’s Group IV). 60.29% of ManTMs have two separate roots (Gulabivala's Groups II and III). The most prevalent types in mesial roots were type I (41.46% in Group II) and type IV (48.78% in Group III), although type I predominated in distal roots (91.24% and 100% in Groups II and III, respectively). Conclusion Single-rooted third molars usually have a root canal morphology that is more favorable for endodontic treatment. In contrast, third molars with fused roots often have more complex root canal morphology.

Objective The aim was to test the Belgrade age formula based on the calculation of open apices of two permanent mandibular teeth on a Bosnian children population and compare its accuracy with European formula. Material and methods We included 412 panoramic images of children (204 female and 208 male) 7 to 13 years of age. We assessed the performance of both methods (the European formula and the BAF) and compared their results in both sexes. Results The results showed a high point of average understanding between the age estimated by chronological age and the European formula (ICC=0.927, 95% CI 0.904–0.944, p<0.001)., BAF also confirmed a high point of agreement with chronological age in boys (ICC=0.941, 95% CI 0.922–0.955, p<0.001) and girls (ICC=0.913, 95% CI 0.886–0.934, p<0.001). BAF was better than the European formula in estimating age in males (0.4448±0.9135 vs. 0.9807±0.9422). Conclusion The Belgrade Age Formula (BAF) demonstrates comparable accuracy to the European formula for age determination in Bosnian children, while offering the advantage of being easier and faster to use. This makes the BAF a practical alternative in clinical and research settings where efficiency and reliability are essential.

Aim: To investigate out-of-hospital cardiac arrest (OHCA) trend, provided advanced life support (ALS) measures, automated external defibrillator (AEDs) utilization and by-standers involvement in cardiopulmonary resuscitation (CPR) during OHCA incidents. Methods: This cross-sectional study encompassed data pertaining to all OHCA incidents attended to by the Emergency Medical Service of Canton Sarajevo, Bosnia and Herzegovina, covering the period from January 2018 to December 2022. Results: Among a total of 1131 OHCA events, 236 (20.8 %) patients achieved return of spontaneous circulation (ROSC); there were 175 (74.1%) males and 61 (25.9%) females. The OHCA incidence was 54/100.000 inhabitants per year. After a 30-day period post-ROSC, 146 (61.9%) patients fully recovered, while 90 (38.1%) did not survive during this timeframe. Younger age (p&lt;0.05), initial rhythm of ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT) (p&lt;0.05) and faster emergency medical team (EMT) response time (p&lt;0.05) were significantly associated with obtaining ROSC. Only 38 (3.3%) OHCA events were assisted by bystanders, who were mostly medical professionals, 25 (65.7%), followed by close family members, 13 (34.3%). There was no report of AED usage. Conclusion: This follow-up study showed less ROSC achievement, similar bystanders&rsquo; involvement, similar factors associated with achieving ROSC (age, EMT response time) and a decline in OHCA events (especially in year 2021 and 2022) comparing to our previous study (2015-2019). There was an extremely low rate of bystander engagement and no AEDs usage. Governments and health organizations must swiftly improve public awareness, promote better practice (basic life support), and actively encourage bystander participation.

Introduction: Neovascular glaucoma (NVG) is a severe type characterized by forming new blood vessels on the iris and the anterior chamber angle, often resulting from ischemic retinal diseases. Pars plana vitrectomy (PPV) is a standard surgical procedure for treating various retinal and vitreous conditions. Understanding the risk factors associated with NVG development following PPV is crucial for improving patient outcomes. Objective: To identify and evaluate demographic, clinical, and surgical risk factors associated with developing NVG following PPV. Patients and methods: A prospective cohort study was conducted over two years, involving 60 type 2 diabetes mellitus (T2DM) patients (31 males and 29 females; mean age 60.48±9.63 years) who underwent PPV at the Eye Clinic and Department of Clinical Immunology, University Clinical Center Sarajevo, Sarajevo, Bosnia and Herzegovina. Patients were thoroughly informed about the study, and written informed consent was obtained. Comprehensive data collection included demographic information, medical history, preoperative and postoperative eye examinations, and intraoperative details. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 21 (Released 2012; IBM Corp., Armonk, New York, United States). Results: Within 12 months postoperatively, 17 patients (28.3%) developed NVG. Significant preoperative risk factors for NVG included prolonged duration of T2DM (p=0.037), elevated preoperative intraocular pressure (IOP) (p=0.024), and higher levels of vascular endothelial growth factor (VEGF) (p=0.011). Intraoperative factors, such as sharp dissection (p=0.000) and operative complications (p=0.004), were also significantly associated with NVG development. Multivariate logistic regression analysis identified prolonged T2DM duration (OR 1.132, p=0.023), increased preoperative IOP (OR 1.192, p=0.029), elevated VEGF levels (OR 1.002, p=0.016), and intraoperative sharp dissection (OR 0.114, p=0.006) as independent risk factors. Conclusions: Multiple preoperative and intraoperative factors influence the development of NVG post-PPV. Prolonged T2DM duration, elevated preoperative IOP, high VEGF levels, and specific intraoperative techniques significantly increase the risk of NVG. These findings underscore the importance of careful preoperative assessment and tailored intraoperative strategies to mitigate NVG risk in PPV patients.

Chylothorax represents the accumulation of chyle in the pleural cavity due to leakage from the thoracic duct or its tributaries. Intraoperative intrathoracic lymphatic injury is a common cause, but it can also occur on its own. Management of chylothorax involves both medical therapy and, in some cases, surgery for postoperative patients and those who haven't responded to medical therapy. We describe a case of a one-month-old female infant with right-sided chylothorax following primary esophageal atresia repair, who underwent successful thoracic duct ligation by open thoracotomy after unsuccessful medical treatment. Minimally invasive radiology is now the standard treatment for traumatic chylothorax because it is safe and effective. However, surgical ligation of the thoracic duct remains an effective option for treating high-output or recurring chylothorax in countries with limited resources.

Abstract This paper introduces a novel method that leverages artificial neural networks to estimate magnetic flux density in the proximity of overhead transmission lines. The proposed method utilizes an artificial neural network to estimate the parameters of a mathematical model that describes the magnetic flux density distribution along the lateral profile for various configurations of overhead transmission lines. The training target data is acquired using the particle swarm optimization algorithm. A performance comparison between the proposed method and the Biot-Savart law-based method is conducted using an extensive test dataset. The resulting coefficient of determination and mean square error values demonstrate the successful application of the proposed method for a range of different spatial arrangements of phase conductors. Furthermore, the performance of the proposed method is thoroughly assessed on multiple test cases. The practical relevance of the proposed method is highlighted by contrasting its results with the field measurements obtained in the proximity of a 400 kV overhead transmission line.

Background: Lactate dehydrogenase (LDH) isoenzyme assay was used widely in the past to diagnose myocardial infarction (MI). Recent studies show that lactate dehydrogenase seems to be a promising biomarker of adverse left ventricular remodeling. Objectives: Higher levels of these biomarkers were associated with lower odds for favorable reverse remodeling in patients with MI. Methods: The study was performed on patients with the first occurrence of acute myocardial infarction (ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (NSTEMI)), aged 34 to 80 years who underwent catheterization at the admission or during their hospital stay depending on indications. In this study, we compared peak levels of lactate dehydrogenase (LDH) and left ventricular ejection fraction (LVEF). Peak values of LDH were used from the second to the fourth day of hospitalization. Echocardiography has been done in the first 72 hours, which represents an early phase of cardiac remodeling. The ejection fraction was evaluated using the Simpson method. Results: Spearman's rank test showed a negative, statistically significant correlation between LDH and ejection fraction ρ(80)=−0.543, p<0.001. Weighted least squares regression model included LDH concentration, age, and type of myocardial infarction (STEMI/NSTEMI), and the slope coefficient for the LDH level was −0.010 (95% confidence interval (CI): −0.013 to −0.006). With each unit of LDH increase, there was a decrease of 0.01% in left ventricular ejection fraction when age and type of myocardial infarction were held constant. Conclusion: The increased LDH level could be a new predictor for early myocardial remodeling after the first occurrence of myocardial infarction independent of age and type of myocardial infarction.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.