BACKGROUND AND OBJECTIVE Peripheral artery disease (PAD) is a progressive vascular condition affecting >237 million individuals worldwide. Accurate diagnosis and patient-specific treatment planning are critical but are often hindered by limited access to advanced imaging tools and real-time analytical support. This study presents DECODE, an open-source, cloud-based platform that integrates artificial intelligence, interactive 3D visualization, and computational modeling to improve the noninvasive management of PAD. METHODS The DECODE platform was designed as a modular backend (Django) and frontend (React) architecture that combines deep learning-based segmentation, real-time volume rendering, and finite element simulations. Peripheral artery and intima-media thickness segmentation were implemented via convolutional neural networks, including extended U-Net and nnU-Net architectures. Centreline extraction algorithms provide quantitative vascular geometry analysis. Balloon angioplasty simulations were conducted via nonlinear finite element models calibrated with experimental data. Usability was evaluated via the System Usability Scale (SUS), and user acceptance was assessed via the Technology Acceptance Model (TAM). RESULTS Peripheral artery segmentation achieved an average Dice coefficient of 0.91 and a 95th percentile Hausdorff distance 1.0 mm across 22 computed tomography dataset. Intima-media segmentation evaluated on 300 intravascular optical coherence tomography images demonstrated Dice scores 0.992 for the lumen boundaries and 0.980 for the intima boundaries, with corresponding Hausdorff distances of 0.056 mm and 0.101 mm, respectively. Finite element simulations successfully reproduced the mechanical interactions between balloon and artery models in both idealized and subject-specific geometries, identifying pressure and stress distributions relevant to treatment outcomes. The platform received an average SUS score 87.5, indicating excellent usability, and an overall TAM score 4.21 out of 5, reflecting high user acceptance. CONCLUSIONS DECODE provides an automated, cloud-integrated solution for PAD diagnosis and intervention planning, combining deep learning, computational modeling, and high-fidelity visualization. The platform enables precise vascular analysis, real-time procedural simulation, and interactive clinical decision support. By streamlining image processing, enhancing segmentation accuracy, and enabling in-silico trials, DECODE offers a scalable infrastructure for personalized vascular care and sets a new benchmark in digital health technologies for PAD.

Wastewaters from the textile industry have a high content of organic matter, high coloration, various minerals and metals, and often toxic and carcinogenic substances. Azo dyes are the most common used dyes in the textile industry. Due to their complex structure, the removal of azo dyes from wastewater is challenging. In this study, electrooxidation and the electro-Fenton process, as one of the most effective Electrochemical Advanced Oxidation Processes (EAOPs) for the removal of organic pollutants in wastewater, were used for the treatment of synthetic wastewater containing “Bemacid Red” dye as a pollutant. Several process parameters affecting the efficiency of OH. formation and dye degradation were examined. Stainless steel (SS) was used as the cathode material, and the anodes used were mixed metal oxides (MMO) - Ru mixed oxide (titanium substrate coated with 6g Ru/m2) and Ru-Ir mixed oxide (titanium substrate coated with 6g Ru- Ir/m2). The results showed that the Ru:SS electrode pair is more efficient comapred to the Ru-Ir:SS pair, and that the electro-Fenton process is more efficient compared to electrooxidation.

In recent years, neurosurgery and clinical neuroscience have undergone a profound transformation, driven by an increasingly interdisciplinary approach that integrates technological innovation, the refinement of therapeutic protocols, and novel rehabilitative paradigms [...].

The liver is a central metabolic organ that regulates numerous physiological processes, including glucose and lipid metabolism, detoxification, and the synthesis of essential proteins and bile. Bile acids (BAs), synthesized from cholesterol in hepatocytes, not only facilitate the emulsification and absorption of dietary fats but also act as potent signaling molecules through receptors such as the farnesoid X receptor (FXR) and Takeda G-protein-coupled receptor 5. Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease globally, closely linked with obesity, insulin resistance, and other components of metabolic syndrome. In MASLD, the metabolism of BAs is markedly disrupted, resulting in alterations in their synthesis, composition, and signaling activity. These changes contribute to hepatic steatosis, inflammation, and fibrosis, thereby exacerbating metabolic dysfunction and liver damage. The altered profiles and signaling activity of BAs in MASLD patients suggest that BAs act not only as biomarkers of disease severity, but also as active mediators of its pathogenesis. Modulators of BA signaling pathways, especially FXR agonists, are the focus of intense research for their potential to beneficially influence liver steatosis and inflammation in MASLD. Recent research has yielded promising results, indicating potential therapeutic application and the introduction of novel agents aimed at modulating BA homeostasis and function. This minireview outlines the physiological roles of BAs, seeks to advance the elucidation of the mechanisms by which their dysregulation contributes to MASLD progression, and highlights current and emerging therapeutic approaches. A deeper understanding of these complex interactions is essential for improving the diagnosis, prognosis and treatment of MASLD.

Background/Objectives: The Reelin–Dab1 signaling pathway, known for its crucial role in neurodevelopment, particularly in neuronal migration and the formation of cortical layers, has been a subject of extensive research. However, its involvement in gastrointestinal organogenesis is a relatively unexplored area. Our study investigates the expression patterns of Dab1, Reelin, PGP9.5, and Sox2 during stomach development in yotari (Dab1−/−) mice and aims to shed light on how Dab1 inactivation affects epithelial–mesenchymal signaling dynamics, thereby contributing to a deeper understanding of this pathway’s non-neural functions. Methods: Embryonic stomach tissues from yotari and wild-type mice, collected at developmental stages E13.5 and E15.5, were examined by immunofluorescenceto evaluate the difference in expression of Dab1, Reelin, PGP9.5, and Sox2. Semi-quantitative scoring and quantitative image analysis were used to assess protein localization and intensity within epithelial and mesenchymal compartments. Results: Dab1 expression was significantly increased in both the epithelium and mesenchyme of yotari mice at E13.5 and E15.5. Reelin expression in the epithelium showed a visible but statistically non-significant decrease in yotari at E15.5, while mesenchymal expression remained low and significantly lower than controls. PGP9.5 expression was significantly reduced in yotari epithelium at E13.5, then strongly upregulated at E15.5. Mesenchymal PGP9.5 remained consistently high. Sox2 showed no statistically significant changes but increased semi-quantitatively in yotari epithelium and mesenchyme at E15.5. These findings highlight compartment-specific disruptions and potential compensatory mechanisms following Dab1 inactivation. Conclusions: Our findings indicate that Dab1 deficiency leads to distinct molecular changes in epithelial and mesenchymal compartments of the developing stomach. The Reelin–Dab1 axis appears critical for epithelial–mesenchymal coordination, while PGP9.5 and Sox2 upregulation in yotari mice may represent potential compensatory responses that could support epithelial integrity, although this remains speculative without functional validation.

We study deterministic, discrete linear time-invariant systems with infinite-horizon discounted quadratic cost. It is well-known that standard stabilizability and detectability properties are not enough in general to conclude stability properties for the system in closed-loop with the optimal controller when the discount factor is small. In this context, we first review some of the stability conditions based on the optimal value function found in the learning and control literature and highlight their conservatism. We then propose novel (necessary and) sufficient conditions, still based on the optimal value function, under which stability of the origin for the optimal closed-loop system is guaranteed. Afterwards, we focus on the scenario where the optimal feedback law is not stabilizing because of the discount factor and the goal is to design an alternative stabilizing near-optimal static state-feedback law. We present both linear matrix inequality-based conditions and a variant of policy iteration to construct such stabilizing near-optimal controllers. The methods are illustrated via numerical examples.

Introduction Ibrutinib has made significant contributions to the treatment of chronic lymphocytic leukemia (CLL) with recognized cardiovascular toxicities in some patients. This study aimed to assess the incidence of cardiotoxicity in CLL patients treated with ibrutinib and identify associated risk factors. Methods This retrospective cohort study analyzed 79 CLL patients treated with ibrutinib at the University Clinical Center of Serbia. Patient characteristics, treatment outcomes, and cardiovascular events were analyzed to determine the incidence of cardiotoxicity and its potential predictors. Results The median age at diagnosis was 58 years, with 63.3% male patients. Pre-existing cardiovascular conditions were present in 55.7% of patients. Cardiotoxicity occurred in 29.1% of patients, with atrial fibrilation developing in 10.1% patients (37.5% grade 3), leading to therapy discontinuation in 62.5% of those affected. Also, we diagnosed hypertension in 15.2%, heart failure in 7.6%, and myocardial infarction in 2.5% of patients. Furthermore, one case (1.3%) of sudden cardiac death was recorded. The administration of ibrutinib was ceased in 9 patients due to cardiotoxic effects. Patients with prior cardiovascular disease had a threefold increased risk of developing cardiotoxicity (HR=2.850; p=0.031). A history of hypertension was significantly associated withthe worsening of hypertension during ibrutinib therapy (HR=7.935; p=0.009). No significant associations were found between cardiotoxicity and other factors such as age, sex, number of prior treatment lines, clinical stage, or cytogenetic abnormalities. Discussion This study underscores the importance of cardiovascular monitoring in CLL patients undergoing ibrutinib therapy, particularly those with pre-existing cardiovascular conditions. These findings highlight the need for individualized treatment planning and close follow-up to mitigate the risk of cardiotoxicity and optimize patient outcomes.

We introduce TROOP, a tree-based Riccati optimistic online planner, that is designed to generate near-optimal control laws for discrete-time switched linear systems with switched quadratic costs. The key challenge that we address is balancing computational resources against control performance, which is important as constructing near-optimal inputs often requires substantial amount of computations. TROOP addresses this trade-off by adopting an online best-first search strategy inspired by A⋆, allowing for efficient estimates of the optimal value function. The control laws obtained guarantee both near-optimality and stability properties for the closed-loop system. These properties depend on the planning depth, which determines how far into the future the algorithm explores and is closely related to the amount of computations. TROOP thus strikes a balance between computational efficiency and control performance, which is illustrated by numerical simulations on an example.

Introduction Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer that accounts for approximately 15% of all lung cancers. Despite advancements in treatment, real-world clinical practice in developing countries often reveals less favorable outcomes than those observed in randomized clinical trials. Material and methods A retrospective analysis was conducted on all patients with extensive-stage SCLC (ES-SCLC) diagnosed or treated at a single center in Bosnia and Herzego-vina. Medical and electronic health records were reviewed to collect data on patients diagnosed with ES-SCLC between 2013 and 2023. The analysis included patient demographics, clinical characteristics, treatment outcomes, and adverse events. Results Ninety-four patients with ES-SCLC were included in the study. Of these, 89.4% were prescribed first-line treatment, and 63.8% received first- line chemotherapy based on cisplatin and etoposide. The median progression- free survival in patients treated with first-line ES-SCLC was five months, with a response rate of 57.5%. The median overall survival of patients treated with first-line chemotherapy in our study was seven months. The most common side effect was hematologic toxicity. Conclusions Our results showed that the outcomes of patients with ES-SCLC in real clinical practice are poor. Further studies of real-world treatment outcomes are essential to validate the findings from randomized controlled trials. Ongoing research is needed to explore strategies for improving outcomes and addressing the unmet needs of patients with ES-SCLC.

Background/Objectives: Children remain underserved in pharmaceutical development, with off-label prescribing still prevalent in part due to a lack of age-appropriate formulations. This study aimed to evaluate the national uptake of Pediatric Use Marketing Authorisation (PUMA)-labelled medicines in Croatia from 2017 to 2024. Methods: We conducted a retrospective, descriptive pharmacoepidemiological study using the IMS (Intercontinental Medical Statistics) and IQVIA (Information, Quintiles, VIA; formerly IMS Health and Quintiles) datasets to track utilization and the expenditure of all PUMA products. Utilization was assessed using defined daily doses per 1000 inhabitants per day (DDDs/1000/day) and annual product dispensation counts. Results: Over the study period, five PUMA medicines entered the Croatian market, with usage rising from 853 packages in 2018 to 9232 in 2024. The DDDs/1000/day increased 33.8-fold, while the expenditure escalated nearly 5.8-fold, from EUR 145,898 to EUR 844,145. Midazolam and melatonin were the most frequently prescribed, yet the overall utilization remained marginal relative to pediatric needs. Conclusions: In conclusion, while regulatory availability of PUMA products has improved, their clinical adoption in Croatia remains limited. Addressing economic, educational, and policy barriers is essential to close the gap between authorization and utilization.

AIM Due to increasing use of mitochondrial DNA (mtDNA) sequencing in both forensic practice and clinical disease research, this study explores the optimization of the next-generation sequencing (NGS) method for whole mitochondrial genome analysis on the Illumina MiSeq platform. METHODS Initial attempts using pre-made commercial primers were unsuccessful, leading to the design of novel custom-designed primers in our laboratory and optimization of sequencing chemistry and protocols. A comprehensive protocol was developed, involving long-range amplification, enzymatic fragmentation, and the use of IDT® for Illumina DNA/RNA UD Indexes and MiSeq Reagent Nano Kit v2 (300 cycles), whereby DNA extraction, quantification, and library preparation were all performed according to optimized protocols. RESULTS Successful amplification was confirmed using gel electrophoresis and Agilent Bioanalyzer, with optimized conditions yielding clear, specific amplicons 9.8 and 8.5 kb in length. Sequencing results demonstrated high-quality reads with an average coverage depth of 742x and a GC content of 43-45%. The study highlights the efficiency of custom primers and individual library normalization for reliable mtDNA sequencing. CONCLUSION These findings advance the application of NGS in forensic and clinical settings by enhancing the detection of rare mutations and mitochondrial heteroplasmy, paving the way for routine mtDNA analysis using NGS technology.

Image‐based plant phenotyping has diverse applications, ranging from providing quantitative traits for genetic breeding to enhancing management practices for indoor and outdoor production systems. Misidentification of cell lines or ecotypes/varieties is a major problem across all biological research disciplines. With the 1000 Arabidopsis Genome Project facilitating the use of various ecotypes, it is crucial to verify the identity of ecotypes in discovery‐based genetic screens involving hundreds of ecotypes. To address this issue, an RGB image analysis pipeline was established for the accurate recognition of different Arabidopsis thaliana ecotypes. In the developed pipeline, the most crucial aspects for accurately capturing traits and training deep learning models were identified as follows: (i) assessment of data complexity using spatial‐temporal features of the RGB spectrum and data entropy, the latter defined as the variability within the dataset; (ii) data redefinition in instances of high data complexity; and (iii) data partitioning based on extracted morphological similarity among ecotype replicates. The pipeline includes several supervised deep learning models integrated into an auto‐optimization subsystem. Extensive hyperparameter tuning was performed to identify the best‐performing models for single‐image and image‐sequence ecotype classification. Two external datasets were evaluated to demonstrate the robustness of the pipeline, regardless of how they were collected. A graphical user interface is provided to prepare these images for input into the pipeline in cases of extreme variability. The pipeline can automatically verify ecotypes in large‐scale studies and extract traits for further analysis and correlation, as needed, using datasets from a variety of sources.

BACKGROUND Acquired demyelinating diseases (ADD) of central nervous system encompasses a wide spectrum of neurological symptoms depending on the location and the severity of demyelination. The aim of this study is to present the frequency and the clinical, immunological, and radiological characteristics of ADD in pediatric patients at the Pediatric Clinic, Clinical Center University of Sarajevo. METHODS This is a retrospective observational study, conducted between 2017-2024, that included patients under 18 years with ADD. The diagnosis is established through clinical evaluation, characteristic MRI findings, immunological markers, and the exclusion of alternative conditions that mimic ADD, following the IPMSSG (International Pediatric Multiple Sclerosis Study Group ) 2010 criteria. We classified the patients into two groups based on the disease course: monophasic, multiphasic group which is further subdivided into multiple sclerosis (MS) and non-MS multiphasic group. RESULTS Forty-one patients with ADD were included in the study. Seventeen patients (17/41, 41.46%) remained monophasic, whereas twenty-four patients (24/41, 58.54%) exhibited a multiphasic course. Within the multiphasic group 22/24 patients (91.67%) were diagnosed with multiple sclerosis (MS), and 2/24 (8.33%) had a non-MS multiphasic disease course. CONCLUSION In this study, we presented the frequency and the clinical, immunological, and radiological characteristics of acquired demyelinating diseases in pediatric patients. Recognizing these distinct clinical patterns is crucial for enhancing early diagnostic accuracy and optimizing management strategies in this patient population. Ultimately, our study supports the need for a prospective, multicentric investigation to further consolidate data and refine our understanding of ADD epidemiology in our region.