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.

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.

BACKGROUND Friendship quality is crucial for psychological well-being, yet is often lower in persons with intellectual disabilities compared to their peers. This study explores the predictors of friendship quality among adults with mild intellectual disability, focusing on age, gender, living setting, psychiatric traits, and social cognition. METHOD The sample comprised 62 adults with mild intellectual disability (32 males, 30 females; ages 19-53), and 30 with significant psychiatric traits. Tools included the Friendship Quality Scale, Edinburgh Social Cognition Test, MINI PAS-ADD scale, and a demographic questionnaire. RESULTS Social cognition and age were the strongest predictors of friendship quality, with higher social cognition scores and younger age correlating with better quality. Gender, living setting, and psychiatric traits had less impact. CONCLUSIONS Social cognition and age are primary determinants of friendship quality in adults with mild intellectual disability, suggesting that interventions to enhance social cognition may benefit this population's social well-being.

Geophysical measurements, such as seismic experiments, are a key target for scientific activities on planetary surfaces. Dense spatial sampling of such measurements is often desirable, and acquisition is traditionally performed at regular intervals. However, achieving regular and dense spatial sampling is made difficult by obstacles and operational constraints of a planetary surface mission. Here, we present an application of compressive sensing (CS) in the design of seismic surveys on planetary surfaces for imaging the shallow subsurface. This approach is based on more flexible, randomized subsampling and requires fewer sources or receivers compared to traditional methods. We illustrate the potential of CS on synthetic data and measurements made along an active seismic transect across a lunar analog site. We then explore the use of CS‐assisted seismic acquisition at a terrestrial analog site in the San Francisco Volcanic Field. We show how irregularly acquired data can be interpolated to reconstruct data at finer spatial sampling and yield seismic images comparable to those from regularly acquired high‐density data. Finally, we apply our approach to reanalyze the legacy data collected by the Active Seismic Experiments during the Apollo 14 and 16 missions. The results show that the CS method can recover missing data and increase the amount of data available for refraction analysis. Our study highlights the potential of CS in future planetary surface exploration missions for (a) an order‐of‐magnitude improvement in survey efficiency and (b) improved imaging quality to gain a deeper understanding of the geologic processes of planetary bodies.

Psoriatic arthritis (PsA) is a chronic inflammatory disease that extends beyond musculoskeletal and dermatologic involvement to elevate cardiometabolic risk. Emerging evidence highlights the critical role of systemic inflammation in metabolic dysregulation, accelerating insulin resistance, dyslipidemia, and oxidative stress, all of which contribute to the increased burden of cardiovascular disease in PsA. This review explores the intricate interplay between inflammatory mediators—such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-17 (IL-17),—adipokine imbalances, and lipid metabolism abnormalities, all of which foster endothelial dysfunction and atherosclerosis. The dysregulation of adipokines, including leptin, adiponectin, and resistin, further perpetuates inflammatory cascades, exacerbating cardiovascular risk. Additionally, the metabolic alterations seen in PsA, particularly insulin resistance and lipid dysfunction, not only contribute to cardiovascular comorbidities but also impact disease severity and therapeutic response. Understanding these mechanistic links is imperative for refining risk stratification strategies and tailoring interventions. By integrating targeted immunomodulatory therapies with metabolic and cardiovascular risk management, a more comprehensive approach to PsA treatment can be achieved. Future research must focus on elucidating shared inflammatory and metabolic pathways, enabling the development of innovative therapeutic strategies to mitigate both systemic inflammation and cardiometabolic complications in PsA.

This paper presents research related to segmentation based on supervisory control, at multiple levels, of optimization of parameters of segmentation methods, and adjustment of 3D microscopic images, with the aim of creating a more efficient segmentation approach. The challenge is how to improve the segmentation of 3D microscopic images using known segmentation methods, but without losing processing speed. In the first phase of this research, a model was developed based on an ensemble of 11 segmentation methods whose parameters were optimized using genetic algorithms (GA). Optimization of the ensemble of segmentation methods using GA produces a set of segmenters that are further evaluated using a two‐stage voting system, with the aim of finding the best segmenter configuration according to multiple criteria. In the second phase of this research, the final segmenter model is developed as a result of two‐level optimization. The best obtained segmenter does not affect the speed of image processing in the exploitation process as its operating speed is practically equal to the processing speed of the basic segmentation method. Objective selection and fine‐tuning of the segmenter was done using multiple segmentation methods. Each of these methods has been subject to an intensive process of a significant number of two‐stage optimization cycles. The metric has been specifically created for objective analysis of segmenter performance and was used as a fitness function during GA optimization and result validation. Compared to the expert manual segmentation, segmenter score is 99.73% according to the best mean segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set). Segmenter score is 99.49% according to the most stable segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set and considering the reference image classes MGTI median, MGTI voter and GGTI).