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.

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.

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.

Congenital lung malformations (CLMs) are rare developmental anomalies of the lung, including congenital pulmonary airway malformations, bronchopulmonary sequestration (BPS), congenital lobar overinflation, bronchogenic cyst, and isolated congenital bronchial atresia. CLMs occur in 4 out of 10,000 live births. BPS can be intralobar or extralobar sequestration. The condition is often misdiagnosed; therefore, more research on the clinical characteristics of pulmonary sequestration should be carried out to improve the preoperative diagnosis rate. The goal of our case report is to increase awareness of this condition, to diagnose and treat it early, so that it is resected before the development of complications appear. We presented a case of a 32‐year‐old female patient who presented with a lung abscess and was diagnosed with pulmonary sequestration.

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.

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.

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.

Background: The purpose of this meta-analysis was to examine the prevalence of hypertensive disorders of pregnancy (HDPs), particularly preeclampsia (PE), in peripartum cardiomyopathy (PPCM)-affected pregnancies, and to evaluate whether a HDP significantly alters the prognosis of PPCM, with specific reference to the recovery of left ventricular function (LVEF) and mortality. Methods: A total of 5468 potentially eligible studies were identified, and 104 were included in the meta-analysis. For pooling proportions, the inverse variance methods with logit transformation were used. Complete recovery of LVEF (>50%) and mortality were expressed by odds ratios (ORs), with 95% confidence intervals (CIs). The Peto OR (POR) was used in cases of rare events. Baseline LV function and baseline LV end-diastolic diameter (LVEDD) were summarized by the mean difference (MD) and 95% confidence interval (CI). Results: The summary estimate of the prevalence of HDPs and PE in women with PPCM was 36% and 25%, respectively. Patients with HDPs and, more specifically, PE with PPCM had a higher chance of complete recovery (OR = 1.87; 95%CI = 1.64 to 2.13; p < 0.001 and OR = 1.98; 95%CI 1.69 to 2.32; p < 0.001, respectively), a higher baseline LVEF (MD, 1.42; 95% CI 0.16 to 2.67; p = 0.03 and MD, 1.69; 95% CI 0.21 to 3.18; p = 0.03, respectively), and a smaller baseline LVEDD (MD, −1.31; 95% CI −2.50 to −0.13; p = 0.03 and MD, −2.63; 95% CI −3.75 to −1.51; p < 0.001, respectively). These results, however, did not translate into a significant difference in 12-month mortality (POR = 0.80; 95% CI = 0.57 to 1.13; p = 0.21 and POR = 1.56; 95% CI 0.90 to 2.73; p = 0.12, respectively). Conclusions: The findings of this study may contribute to evidence that can be utilized to aid in the risk stratification of patients with PPCM regarding their long-term prognoses.

Background A comprehensive understanding of the anatomy and pathology of the maxillary sinus is paramount for precise diagnosis and effective planning of dental interventions. Cone Beam Computed Tomography (CBCT) offers enhanced visualization of sinus structures, thus facilitating the identification of anatomical variations and pathological conditions which are critical for surgical and dental treatment strategies. Materials and Methods This retrospective descriptive study analyzed 200 CBCT scans, comprising 400 maxillary sinuses from patients treated at the Faculty of Dentistry University of Sarajevo. The study assessed sinus dimensions, volumes, anatomical features, their relationships with adjacent anatomical structures, and the occurrence of pathological alterations. Furthermore, the patient's gender and dental status were studied in relation to these features. The sinus measurements were performed with Sidexis 4 software (Dentsply Sirona, Germany), which is intended for precise linear measurements in three orthogonal planes. Results Significant differences were found in sinus width between patients with full dentition and those with partial or complete edentulism (p<0.01). Male participants exhibited larger mean sinus dimensions compared to females in all dimensions. The maxillary sinus floor was inferior to the nasal floor in 91.5% of cases. Various types of sinus membrane abnormalities were observed, with normal membrane thickness in 53.75% of cases. Sinus septa were most frequently located on the roof of the sinus, and their frequency varied significantly among patients with different dentition statuses. The study also identified 274 Haller cells and documented several pathological changes, with mucosal thickening exceeding 3 mm being the most common alteration. Conclusion This radiographic study of Bosnian and Herzegovian population revealed significant anatomical variations and pathological changes in maxillary sinuses, thus emphasizing the importance of careful preoperative evaluation using CBCT for surgical planning in the posterior maxillary area. The findings highlight gender-based differences in sinus volumes, the impact of dentition status on sinus anatomy, and the prevalence of various pathological conditions, thus contributing to valuable insights in the field of maxillofacial radiology.

Background: Myocardial injury (MI) is characterized by an increased level of at least one cardiac troponin. Experimental MI can be induced by isoprenaline, a β-adrenergic agonist, and it can lead to heart failure (HF). Liraglutide is glucagon-like 1 peptide receptor agonist used in diabetes management, but it has anti-inflammatory and antioxidative effects, which can be beneficial in treatment of HF. The aim of this study was to investigate the effects of liraglutide on isoprenaline-induced MI and prevention of HF. Methods: Male Wistar albino rats were divided into four groups: Con—received saline the first 2 days + saline the next 7 days; Iso—isoprenaline the first 2 days + saline the next 7 days; Lir—saline the first 2 days + liraglutide the next 7 days; Iso + Lir—isoprenaline the first 2 days + liraglutide the next 7 days. On day 10, blood samples were taken for biochemical analysis and oxidative stress marker evaluation, and hearts were isolated for pathohistological analysis. Cardiac function was assessed by electrocardiography (ECG) and echocardiography (ECHO). Results: Liraglutide treatment significantly attenuated oxidative stress, repaired ECG and ECHO parameters, and mitigated myocardial morphological changes induced by isoprenaline. Conclusions: Liraglutide restores cardiac function in isoprenaline-induced HF.