Atherosclerosis is the underlying cause of myocardial infarction and ischemic stroke. It is a lipid-triggered and cytokine/chemokine-driven arterial inflammatory condition. We identify D-dopachrome tautomerase/macrophage migration-inhibitory factor-2 (MIF-2), a paralog of the cytokine MIF, as an atypical chemokine promoting both atherosclerosis and hepatic lipid accumulation. In hyperlipidemic Apoe–/– mice, Mif-2-deficiency and pharmacological MIF-2-blockade protect against lesion formation and vascular inflammation in early and advanced atherogenesis. MIF-2 promotes leukocyte migration, endothelial arrest, and foam-cell formation, and we identify CXCR4 as a receptor for MIF-2. Mif-2-deficiency in Apoe–/– mice leads to decreased plasma lipid levels and suppressed hepatic lipid accumulation, characterized by reductions in lipogenesis-related pathways, tri-/diacylglycerides, and cholesterol-esters, as revealed by hepatic transcriptomics/lipidomics. Hepatocyte cultures and FLIM-FRET-microscopy suggest that MIF-2 activates SREBP-driven lipogenic genes, mechanistically involving MIF-2-inducible CD74/CXCR4 complexes and PI3K/AKT but not AMPK signaling. MIF-2 is upregulated in unstable carotid plaques from atherosclerotic patients and its plasma concentration correlates with disease severity in patients with coronary artery disease. These findings establish MIF-2 as an atypical chemokine linking vascular inflammation to metabolic dysfunction in atherosclerosis.

Introduction This study aimed to investigate the anti-inflammatory, antioxidant, and anti-apoptotic properties of ursodeoxycholic (UDCA) and chenodeoxycholic (CDCA) bile acids in a rat model of endotoxin (lipopolysaccharide, LPS)-induced acute lung injury (ALI). Methods The study included six groups of Wistar rats exposed to different pretreatments. The control and endotoxin groups were pretreated with propylene glycol, a solvent for bile acids, while the other groups received UDCA or CDCA for 10 days. On the 10th day, an endotoxin injection was given to evaluate the impact of these pretreatments. Lung tissue sections were analyzed by immunohistochemistry, targeting the pro-inflammatory marker nuclear factor kappa B (NF-κB), the anti-apoptotic marker B-cell lymphoma 2 (BCL-2), pro-apoptotic markers BCL-2-associated X protein (BAX) and caspase 3, as well as the aquaporins 1 and 5 (AQP1 and AQP5). Oxidative stress was assessed in bronchoalveolar lavage fluid (BALF). Results and discussion This study demonstrates that UDCA and CDCA can mitigate endotoxin-induced lung injury in rats. These effects are achieved through modulation of AQP1 and AQP5 expression, reduction of oxidative stress, regulation of apoptotic pathways (BAX, caspase 3, BCL-2), and attenuation of pro-inflammatory activity of NF-κB. Although the results indicate a significant association between the expression of these proteins and histopathological changes, the potential influence of additional factors cannot be excluded. These findings suggest that UDCA and CDCA provide lung protection by acting through complex mechanisms involving inflammatory, oxidative, and apoptotic pathways.

Architecture embodies the social context from which it emerges. In the countries of the former Yugoslavia, architects and planners have played a pivotal role in translating the ideals and values of political systems into physical space. The socialist programs of “brotherhood and unity” and “worker self-management” were articulated in various public architectural typologies, open and accessible to all, and shaped a new social framework. Less emphasized but equally present is the historical continuity of self-organizing architecture, representing the shared goal of population survival and adaptability to forthcoming changes. In the aftermath of the 1990s war, Bosnia and Herzegovina is undergoing a multifaceted transition: from socialism to capitalism, from conflict to peace, from post-war recovery toward sustainable development and democratic governance. More than 30 years later, this radical paradigm shift has significantly impacted the urban landscape of Sarajevo, affecting both new developments and the approach to the urban legacy of previous epochs. By correlating the socio-spatial factors of transition, this article explores the post-socialist residential neighborhoods of Novo Sarajevo that were once divided by the frontline during the siege of Sarajevo, particularly their current status and the potential for the transformation of the remaining indoor and outdoor social spaces. The model employed for redefining social spaces in vulnerable areas emphasizes user participation, and was tested through an academic research project to address collective issues. This research has shown the role of the participatory approach as an instrument for the reinvention of existing, even contested, social assets to create an inclusive, sustainable urban environment in post-conflict conditions. The approach may be able to heal the remnants of the collapsed system, its neglected legacy, and the damaged urban and social structures.

Magnetometry is used to detect ferrous objects at various scales, but detecting small-size, compact sources that produce small-amplitude anomalies in the shallow subsurface remains challenging. Magnetic anomalies are often approximated as dipoles or volumes of dipoles that can be located, and their source parameters (burial depth, magnetization direction, magnetic susceptibility, etc.) are characterized using scalar or vector magnetometers. Both types of magnetometers are affected by space weather and cultural noise sources that map temporal variations into spatial variations across a survey area. Vector magnetometers provide more information about detected bodies at the cost of extreme sensitivity to orientation, which cannot be reliably measured in the field. Magnetic gradiometry addresses the problem of temporal-to-spatial mapping and reduces distant noise sources, but the heading error challenges remain, motivating the need for magnetic gradient tensor (MGT) invariants that are relatively insensitive to rotation. Here, we show that the finite size of magnetic gradiometers compared to the lengthscales of magnetic anomalies due to small buried objects affects the properties of the gradient tensor, including its symmetry and invariants. This renders traditional assumptions of magnetic gradiometry largely inappropriate for detecting and characterizing small-size anomalies. We then show how the properties of the finite-difference MGT and its invariants can be leveraged to map these small sources in the shallow critical zone, such as unexploded ordnance (UXO), landmines, and explosive remnants of war (ERW), using both synthetic and field data obtained with a triaxial magnetic gradiometer (TetraMag).

The subpath number of a graph G is defined as the total number of subpaths in G, and it is closely related to the number of subtrees, a well-studied topic in graph theory. This paper is a continuation of our previous paper [5], where we investigated the subpath number and identified extremal graphs within the classes of trees, unicyclic graphs, bipartite graphs, and cycle chains. Here, we focus on the subpath number of cactus graphs and characterize all maximal and minimal cacti with n vertices and k cycles. We prove that maximal cacti are cycle chains in which all interior cycles are triangles, while the two end-cycles differ in length by at most one. In contrast, minimal cacti consist of k triangles, all sharing a common vertex, with the remaining vertices forming a tree attached to this joint vertex. By comparing extremal cacti with respect to the subpath number to those that are extremal for the subtree number and the Wiener index, we demonstrate that the subpath number does not correlate with either of these quantities, as their corresponding extremal graphs differ.

This study aims to measure digital financial literacy of MSME managers and to analyse the relationship between business experience, digital financial literacy and fintech behavioural adoption. The direct and indirect effects of business experience to fintech behavioural adoption are being explored. Dataset from UNSA 2023 Survey of MSME managers’ financial literacy in Sarajevo Canton, Bosnia and Herzegovina, using cross-sectional research design has been utilized. The main methodology relies on Principal Component Analysis, regression analysis and PROCESS method for analysing mediation effects. The findings indicate that the effect of business experience on fintech behavioural adoption is fully mediated by digital financial literacy. Moreover, there is a full serial mediation effect through all three digital financial literacy components, digital financial knowledge, attitudes and behaviour, in a sequence. Interestingly, full mediation is evident also through only digital financial behaviour. To increase fintech adoption, financial institutions should focus on enhancing digital financial literacy and the adept behaviours of MSME managers. These efforts can be leveraged to effectively market and sell fintech products. Policy implications are seen in the need for strengthening overall digital financial literacy competencies of managers and increasing financial inclusion of MSMEs. Regulators should draw effective policies therefore. Educational programs should be directed toward enhancing digital financial knowledge and positive attitudes and behaviour of MSME managers, especially focusing on aged managers, but also on those with short managerial experience. This study makes a unique contribution to the limited empirical evidence of the mediation role of digital financial literacy and its components in the relationship between business experience and fintech behavioural adoption. Digital financial literacy, all three digital financial literacy components in a sequence, and digital financial behaviour serve as mediators in this relationship.

Enzymes are catalysts of biological origin, and according to their chemical composition, they are simple or complex proteins. There are several theories about the enzyme's mechanism of action. Today, the Michaelis-Menten theory is generally accepted. According to this theory, during enzymatic reactions, an intermediate compound is created between the enzyme and the substrate. After the formation of this complex, the enzyme catalyzes a chemical reaction that changes the substrate into another molecule, which we call the product. The product is then separated and released from the active site of the enzyme, which is then ready to bind the next substrate molecule. Enzyme activity can be affected by different molecules. The purpose of this study is to use the spectrophotometric approach to determine whether sodium benzoate and ascorbic acid (vitamin C) serve as activators or inhibitors of enzymatic reactions. The obtained results show that both additives bind to the enzyme-substrate complex, causing non-competitive inhibition.

A critical predictive marker for anti-PD-1/PD-L1 therapy is programmed death-ligand 1 (PD-L1) expression, assessed by immunohistochemistry (IHC). This paper explores a novel automated framework using deep learning to accurately evaluate PD-L1 expression from whole slide images (WSIs) of non-small cell lung cancer (NSCLC), aiming to improve the precision and consistency of tumor proportion score (TPS) evaluation, which is essential for determining patient eligibility for immunotherapy. Automating TPS evaluation can enhance accuracy and consistency while reducing pathologists’ workload. The proposed automated framework encompasses three stages: identifying tumor patches, segmenting tumor areas, and detecting cell nuclei within these areas, followed by estimating the TPS based on the ratio of positively stained to total viable tumor cells. This study utilized a Reference Medicine (Phoenix, Arizona) dataset containing 66 NSCLC tissue samples, adopting a hybrid human–machine approach for annotating extensive WSIs. Patches of size 1000 × 1000 pixels were generated to train classification models, such as EfficientNet, Inception, and Vision Transformer models. Additionally, segmentation performance was evaluated across various UNet and DeepLabV3 architectures, and the pre-trained StarDist model was employed for nuclei detection, replacing traditional watershed techniques. PD-L1 expression was categorized into three levels based on TPS: negative expression (TPS < 1%), low expression (TPS 1%–49%), and high expression (TPS ≥ 50%). The Vision Transformer-based model excelled in classification, achieving an F1-score of 97.54%, while the modified DeepLabV3+ model led in segmentation, attaining a Dice Similarity Coefficient of 83.47%. The TPS predicted by the framework closely correlated with the pathologist’s TPS at 0.9635, and the framework’s three-level classification F1-score was 93.89%. The proposed deep learning framework for automatically evaluating the TPS of PD-L1 expression in NSCLC demonstrated promising performance. This framework presents a potential tool that could produce clinically significant results more efficiently and cost-effectively.

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. As infant incubators are used as a form of fundamental healthcare support for the most sensitive population, prematurely born infants, special care mus be taken to ensure their proper functioning. This is done through a standardized process of post-market surveillance. Objective To address the issue of faulty infant incubators being undetected and used between yearly post-market surveillance, an automated system based on machine learning was developed for prediction of infant incubator performance status. Methods In total, 1997 samples were collected during the inspection process of infant incubator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) for the development of the automated system. Results The aforementioned algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The 0.93 AUC of Naïve Bayes indicates that it is overall stronger in predictive capabilities than decision tree and random forest which displayed superior accuracy in comparison to Naïve Bayes. Conclusion The results of this study demonstrate that machine learning algorithms can be effectively used to predict infant incubator performance status on the basis of measurements taken during post-market surveillance. Adoption of these automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of infant incubators that are already being used in healthcare institutions.

Background Heart failure (HF) is characterized by impaired cardiac function. Based on left ventricular ejection fraction (LVEF), it is classified into HF with reduced ejection fraction (HFrEF), mildly reduced ejection fraction (HFmrEF), and preserved ejection fraction (HFpEF). Each phenotype has distinct pathophysiological mechanisms and clinical features. Recent findings indicate that systemic inflammation is a significant factor in the progression of heart failure. Inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and lymphocyte-to-monocyte ratio (LMR), may serve as valuable tools for evaluating the inflammatory response in heart failure. Materials and methods This prospective observational study, which included 171 HF patients, was conducted from February 2022 to January 2023 at the Intensive Care Unit, University Clinical Centre Tuzla. Based on LVEF, patients were categorized into HFrEF, HFmrEF, and a control group (HFpEF). The study aimed to assess the prognostic value of NLR, MLR, and LMR in predicting major adverse cardiovascular events (MACE) and mortality over a 12-month follow-up period. Results NLR and MLR were significantly higher, while LMR was lower in both HFrEF and HFmrEF compared to controls, indicating a strong inflammatory response, particularly in HFrEF. NLR demonstrated a strong ability to distinguish between HF phenotypes. HFmrEF's markedly higher high-sensitivity troponin I (hsTroponin I) level suggested higher cardiac stress. MACE rates were similar across groups; mortality was significantly higher in HFrEF. Conclusion Inflammatory biomarkers NLR, MLR, LMR, and hsTroponin I could be crucial in assessing heart failure, particularly in patients with HFrEF and HFmrEF.

Introduction Nurse anesthetists (NAs) rely on various tools to perform their daily tasks effectively, with communication being one of the most essential during the perioperative phase. The study aimed to explore NAs' experiences with the perioperative dialogue with patients and how this dialogue has evolved over the past 30 years. Materials and methods The study employed a qualitative design, with data gathered through three group interviews focusing on NAs' experiences. Interpretive content analysis, following the approach of Graneheim and Lundman, was used. Initially, 27 NAs were recruited, and 18 (three men and 15 women) participated in the interviews. Their ages ranged from 33 to 72 years, with work experience spanning 17 to 42 years. Results The text analysis identified three categories: advantages of perioperative dialogue, disadvantages of its absence, and suggestions for improvement. Key challenges included maintaining continuity of care, ensuring a high level of patient and NA safety, reducing care-related complications, minimising patient socialisation, providing incomplete care, and increasing stress for both NAs and patients. The NAs also offered several suggestions for improvement. Conclusion Perioperative meetings should be better structured to improve communication and assess outcomes. Enhancing patient involvement, developing NAs' skills, and providing clearer information in multiple languages could improve satisfaction and safety. Further research is needed to establish the dialogue’s role as a guiding principle for staff and patients.

Dynamic programming (DP) is a fundamental algorithmic strategy that decomposes large problems into manageable subproblems. It is a cornerstone of many important computational methods in diverse fields, especially in the field of computational genomics, where it is used for sequence comparison. However, as the scale of the data keeps increasing, these algorithms are becoming a major computational bottleneck, and there is a need for strategies that can improve their performance. Here, we present Vectron, a novel auto-vectorization suite that targets array-based DP implementations written in Python and converts them to efficient vectorized counterparts that can efficiently process multiple problem instances in parallel. Leveraging Single Instruction Multiple Data (SIMD) capabilities in modern CPUs, along with Graphics Processing Units (GPUs), Vectron delivers significant speedups, ranging from 10% to more than 20x, over the conventional C++ implementations and manually vectorized and domain-specific state-of-the-art implementations, without necessitating large algorithm or code changes. Vectron's generality enables automatic vectorization of any array-based DP algorithm and, as a result, presents an attractive solution to optimization challenges inherent to DP algorithms.