Introduction: Perindopril is a tissue-specific ACE inhibitor with 24 hours long blood pressure-lowering effect, which protects blood vessels and decreases the variability of blood pressure. Aim: The aim of our study was to investigate the effectiveness and safety of perindopril in newly diagnosed or previously treated but uncontrolled adult hypertensive patients. Methods: This prospective cohort study included primary care patients with essential hypertension. Primary study outcomes were decreasing arterial blood pressure to normal levels (<140/90 mmHg), reducing systolic arterial blood pressure for 10 mmHg or more and reducing diastolic arterial blood pressure for 5 mmHg or more. Safety was evaluated by type and frequency of adverse events. Results: In the great majority of the study patients (more than 96%) perindopril was effective as monotherapy, achieving a significant reduction in both systolic and diastolic blood pressure, and in three-quarters of the study patients it normalized both systolic and diastolic blood pressure. The effectiveness of perindopril was shown in both patients with previously and newly diagnosed hypertension, adverse events were mild and rare, even hyperkalemia was encountered less often than before the onset of the therapy with perindopril. Conclusions: Our study confirmed excellent effectiveness of perindopril in the treatment of essential hypertension and its remarkable safety. When used as monotherapy of hypertension, perindopril’s doses should be carefully titrated until the achievement of full effect, which in some patients should be awaited for at least 6 months from onset of the therapy.

Introduction: Osteoporosis is a chronic progressive bone disease where the bone tissue resorption exceeds its regenerative capacities. Such a process leads to the reduction of bone mineral density (BMD), and distortion of trabecular microarchitectonics, which creates the basis for an increased fracture risk on a “low trauma” for osteoporosis patients. The notion of low trauma implies a stressor that will not cause a fracture in a healthy person under normal circumstances. BMD is a strong predictor of future fractures. However, many fractures occur in persons with BMD values beyond the defined osteoporosis threshold, and BMD measurement only partially identifies the part of the population with increased fracture risk. Also, it is known that risk factors are influencing the bone mass reduction as predictors of future fractures, and their association may lead to an increased fracture risk irrespective of the bone mass and T-score. Aim: The 10-year individual risk assessment for osteoporotic fracture and the analysis of impact of individual and multiple osteoporosis risk factors on the degree of osteoporotic fracture risk. Methods: The research is a retrospective-prospective study which analyzed 120 patients divided into two groups: 1) asymptomatic patients with known risk factors for osteoporosis in the age group of 40-65 (n=60), 2) asymptomatic patients with known risk factors for osteoporosis in the age group of 65-90 (n=60). FRAX® algorithm was used as a tool for the 10-year hip fracture risk assessment, with prior approval of the Centre for Metabolic Bone Diseases, University of Sheffield from the United Kingdom. Fracture risk assessment was calculated using the online FRAX® calculator. High risk is defined as the hip fracture risk higher than 3% or the risk of a “big” osteoporotic fracture higher than 20%. Results are expressed as mean values with a standard deviation. A comparison between tested patient groups was made applying the student T-test. Results: 32% of patients of average age of 65.8±12.6 years are under high hip fracture risk, 28% of patients are under the hip fracture risk higher than 3%, and the risk for 0.03% patients is higher than 20%. Patients with high fracture risk are of advanced age, female, with lower body weight and height values, lower bone mineral density (BMD) and T score values than patients who are not under a high fracture risk. A positive family anamnesis to osteoporosis and fractures, earlier fractures, smoking, rheumatoid arthritis, and use of glucocorticoids are risk factors that are more represented in patients with high fracture risk and osteoporosis. The impact of the majority of individual risk factors for osteoporosis and fracture is moderate, and their joint effect is significant. The contribution of individual risk factors to the overall 10-year fracture risk depends on the type, number and association of risk factors. Conclusion: This research is a contribution to the resolution of polemics among authors, i.e. a dilemma whether persons with multiple clinical risk factors for osteoporosis with T score values beyond the defined threshold for osteoporosis are candidates for therapy with bisphosphonates, and a dilemma whether persons without any clinical risk factors for osteoporosis with T score values within the defined osteoporosis threshold require therapy with bisphosphonates, or only monitoring is sufficient.

Abstract The purpose of the present research is to explore the effect of brand image on brand loyalty and brand commitment to national and private label brands. Data was collected through a field survey via the store-intercept method. To test the research hypotheses, we used two samples, which implied the collection of usable 528 questionnaires. Confirmatory factor analysis was used to test the reliability, convergent, and discriminant validity of the constructs of interest. The structural equation modeling (SEM) technique was employed to analyze the effect of brand image on brand loyalty and brand commitment. Also, group comparisons were made between national brands and private label brands, using structural equation modeling. This study reveals that brand image has a significant positive effect on brand loyalty and brand commitment, both for national and private label brands. Moreover, it was found that the effect of brand image on brand commitment is stronger for national brands than for private label brands. The present study provides marketing managers of national brands and private label brands some insights into how to spur brand loyalty and commitment by establishing a favorable brand image.

New technologies introduce more services which will call for significant advances and changes in IP networks. Networks will have to go in the direction of network programmability, virtualization and cloud-based services. In this paper, Quality of Service (QoS) is configured in two different environments: (i) in traditional IP network using QoS policy-maps and (ii) in Software Defined Network. Two scenarios are compared in order to emphasize the benefits of using SDN technology when providing QoS provisioning mechanisms.

Multiprotocol Label Switching Traffic-Engineering (MPLS TE) is a mechanism for optimizing network performances by using existing routing and resource reservation protocols in order to calculate and establish tunnels used for transporting different traffic flows depending on their destination. Aim of this paper is presenting benefits of using Segment Routing Traffic Engineering (SR-TE) in Software Defined Network (SDN) environment in order to overcome several MPLS TE problems and challenges presented in this paper. In order to present benefits of SR-TE in SDN, two scenarios are compared. In the first scenario SR is implemented in classical IP/MPLS network. In the second scenario, SDN controller is added to a topology and control over paths is delegated to SDN controller.

In this review, we highlight information on microRNA (miRNA) identification and functional characterization in the beef for muscle and carcass composition traits, with an emphasis on Qinchuan beef cattle, and discuss the current challenges and future directions for the use of miRNA as a biomarker in cattle for breeding programs to improve meat quality and carcass traits. MicroRNAs are endogenous and non-coding RNA that have the function of making post-transcriptional modifications during the process of preadipocyte differentiation in mammals. Many studies claim that diverse miRNAs have an impact on adipogenesis. Furthermore, their target genes are associated with every phase of adipocyte differentiation. It has been confirmed that, during adipogenesis, several miRNAs are differentially expressed, including miR-204, miR-224, and miR-33. The development of mammalian skeletal muscle is sequentially controlled by somite commitment into progenitor cells, followed by their fusion and migration, the proliferation of myoblasts, and final modification into fast- and slow-twitch muscle fibers. It has been reported that miRNA in the bovine MEG3-DIO3 locus has a regulatory function for myoblast differentiation. Likewise, miR-224 has been associated with controlling the differentiation of bovine adipocytes by targeting lipoprotein lipase. Through the posttranscriptional downregulation of KLF6, miR-148a-3p disrupts the proliferation of bovine myoblasts and stimulates apoptosis while the miR-23a~27a~24-2 cluster represses adipogenesis. Additional to influences on muscle and fat, bta-mir-182, bta-mir-183, and bta-mir-338 represent regulators of proteolysis in muscle, which influences meat tenderness.

The rapid development of financial markets results in data variability and unpredictability. Anomaly detection in financial data is a very important issue. Finding anomalies can result in error reduction and corrections in due time. The main aim of this research was to find anomalies in general ledgers of a real company in Bosnia and Herzegovina. Anomalies are defined as input errors of accountants. Main concepts of anomaly detection are defined, a summary of the current progress is given, and challenges of future work are presented. Cluster-based and histogram-based anomaly detections were performed on a real-life dataset of a microcredit organization. Results of algorithms were presented, as well as results achieved using synthetic data.

The highly dynamic wireless communication environment poses a challenge for many applications (e.g., adaptive multimedia streaming services). Providing accurate TP can significantly improve performance of these applications. The scheduling algorithms in cellular networks consider various PHY metrics, (e.g., CQI) and throughput history when assigning resources for each user. This article explains how AI can be leveraged for accurate TP in cellular networks using PHY and application layer metrics. We present key architectural components and implementation options, illustrating their advantages and limitations. We also highlight key design choices and investigate their impact on prediction accuracy using real data. We believe this is the first study that examines the impact of integrating network-level data and applying a deep learning technique (on PHY and application data) for TP in cellular systems. Using video streaming as a use case, we illustrate how accurate TP improves the end user's QoE. Furthermore, we identify open questions and research challenges in the area of AI-driven TP. Finally, we report on lessons learned and provide conclusions that we believe will be useful to network practitioners seeking to apply AI.

Abstract The main objective of this investigation was to determine and summarize the economic burden of severe COPD exacerbations that required hospitalization and the difference in the costs of treatment between patients with frequent (at least two exacerbations in one year) and infrequent exacerbation. Our results suggested that significantly more resources had to be spent to treat patients with at least two hospitalizations during the study related to the use of medications primarily affecting the respiratory system (corticosteroids, p = 0.013, theophylline, p = 0.007) and total hospital stay (31336.68 ± 19140 RSD/517.53 ± 316.1 EUR versus 23650.15 ± 14956.0 RSD/390.59 ± 247 EUR, p=0.002) compared to patients who stayed in a semi-intensive care unit (12875.35 ± 20742.54 RSD versus 4310.62 ± 9779.78 RSD/ 212.64 ± 342.57 EUR versus 71.19 ± 161.51 EUR, p=0.006). Based on the total number of days in the hospital, the costs of the drugs, the materials used and services provided, patients from the frequent exacerbation group had significantly higher costs (80034.1 ± 36823.7 RSD/1321.78 ± 608.15 EUR versus 69425.5 ± 34083.1 RSD/1146.58 ± 562.89 EUR) comparedthan patients in the infrequent exacerbation group (p=0.039). Our results indicate that significantly more funds will be spent treating the deterioration of patients who stay longer in the hospital or in the semi-intensive care unit. Their condition will require a significantly greater use of drugs that are primarily used to treat the respiratory system and, therefore, will utiliseutilize significantly more resources.

We present explicit formulas for the Laplacian Szeged eigenvalues of paths, grids, $C_4$-nanotubes and of Cartesian products of paths with some other simple graphs. A number of open problems is listed.

The framework FRADIR is designed to improve disaster resilience for mission-critical applications through reliable network design, regional failure modeling and protection routing. Two different failure models are jointly considered: independent random failures and regional failures, which are used to model the effects of disasters. In this paper we provide the first availability analysis of FRADIR. We examine the effect of the Spine and protection routing on end-to-end availability. In particular the 1+1 and four versions of General Dedicated Protection (GDP) are utilized to demonstrate the bandwidth allocation, blocking probability and availability trade-off. After the comprehensive availability analysis, to take control over this trade-off we introduce a novel routing method for FRADIR, called Availability Constraint FRADIR (AC-FRADIR) which not only minimizes the bandwidth cost and ensures protection against certain failure events (listed in the SRLG list), but also guarantees that after any of these failure events the remaining communication path still satisfies a certain availability level.

Gene therapy applications of oncolytic viruses represent an attractive alternative for cancer treatment. A broad range of oncolytic viruses, including adenoviruses, adeno-associated viruses, alphaviruses, herpes simplex viruses, retroviruses, lentiviruses, rhabdoviruses, reoviruses, measles virus, Newcastle disease virus, picornaviruses and poxviruses, have been used in diverse preclinical and clinical studies for the treatment of various diseases, including colon, head-and-neck, prostate and breast cancer as well as squamous cell carcinoma and glioma. The majority of studies have focused on immunotherapy and several drugs based on viral vectors have been approved. However, gene therapy for malignant melanoma based on viral vectors has not been utilized to its full potential yet. This review represents a summary of the achievements of preclinical and clinical studies using viral vectors, with the focus on malignant melanoma.