Multiple sclerosis (MS) is a chronic, inflammatory, (auto) immune disease of the central nervous system (CNS). The aim of the study was to determine the distribution of cognitive disorders in MS in relation to demographic parameters, degree of clinical disability and depression. The prospective study included 135 subjects with MS in the Clinic of Neurology of the University Clinical Center in Tuzla. The first group consisted of women (101 respondents) and the second of men (34 respondents). Clinical assessment instruments were: Expanded Disability Status Scale Score, Mini Mental Status, Beck Depression Scale, Battery of Cognitive Function Assessment Tests: Wechsler Intelligence Scale, Revised Beta Test, Raven Coloured Progressive Matrix, Wechsler Memory Verification Scale, Audio Memory Test learning, Rey-Osterriecht complex character test, verbal fluency test. There were no significant differences between the mentioned groups in age, level of education, duration of the disease, severity of disease symptoms or in the prevalence of certain forms of MS. Cognitive disorders are present in 40-60 % of subjects with MS. Visuospatial, visuoconstructive, visuoperceptive functions, mnestic functions were most affected in both groups of respondents. There was no difference in the level of depression in relation to sex. Poor results of cognitive parameters in 32.7 % can be considered the cause of high scores of EDSS in female patients and in 29.2 % in patients, which is not statistically significant. The correlation between depression and EDSS is positive but not statistically significant in both sexes. Cognitive disorders are heterogeneous regardless of sex. Cognitive impairment in MS patients is related to impairment of working ability and memory, executive functions and attention. Subjects with a more severe degree of clinical disability had poorer cognitive functions.

Background Bosnia and Herzegovina (BiH) and Serbia are countries in the Western Balkans that share parts of their social and political legacy from the former Yugoslavia, such as their health care system and the fact that they are not members of European Union. There are very scarce data on COVID – 19 pandemic from this region when compared to other parts of the world and even less is known about its impact on the provision of renal care or differences between countries in the Western Balkans. Materials and methods This observational prospective study was conducted in two regional renal centres in BiH and Serbia, during the COVID – 19 pandemic. We obtained demographic and epidemiological data, clinical course and outcomes of dialysis and transplant patients with COVID – 19 in both units. Data were collected a via questionnaire for two consecutive time periods: February – June 2020 with a total number of 767 dialysis and transplant patients in the two centres, and July – December 2020 with a total number of 749 studied patients, corresponding to two of the largest waves of the pandemic in our region. Departmental policies and infection control measures in both units were also recorded and compared. Results For a period of 11 months, from February to December 2020, 82 patients on in-centre haemodialysis (ICHD), 11 peritoneal dialysis patients and 25 transplant patients who tested positive for COVID-19. In the first study period, the incidence of COVID – 19 positive in Tuzla was 1.3% among ICHD patients, and there were no positive peritoneal dialysis patients, or any transplant patients who tested positive. The incidence of COVID-19 was significantly higher in both centres in the second time period, which corresponds to the incidence in general population. Total deaths of COVID-19 positive patients was 0% in Tuzla and 45.5% in Niš during first, and 16.7% in Tuzla and 23.4% in Niš during the second period. There were notable differences in the national and local/departmental approach to the pandemic between the two centres. Conclusion There was poor survival overall when compared to other regions of Europe. We suggest that this reflects the lack of preparedness of both of our medical systems for such situations. In addition, we describe important differences in outcome between the two centres. We emphasize the importance of preventative measures and infection control and highlight the importance of preparedness.

Domain-specific languages (DSLs) are able to provide intuitive high-level abstractions that are easy to work with while attaining better performance than general-purpose languages. Yet, implementing new DSLs is a burdensome task. As a result, new DSLs are usually embedded in general-purpose languages. While low-level languages like C or C++ often provide better performance as a host than high-level languages like Python, high-level languages are becoming more prevalent in many domains due to their ease and flexibility. Here, we present Codon, a domain-extensible compiler and DSL framework for high-performance DSLs with Python's syntax and semantics. Codon builds on previous work on ahead-of-time type checking and compilation of Python programs and leverages a novel intermediate representation to easily incorporate domain-specific optimizations and analyses. We showcase and evaluate several compiler extensions and DSLs for Codon targeting various domains, including bioinformatics, secure multi-party computation, block-based data compression and parallel programming, showing that Codon DSLs can provide benefits of familiar high-level languages and achieve performance typically only seen with low-level languages, thus bridging the gap between performance and usability.

In response to the significant public health threat caused by coronavirus disease (COVID-19), real-time surveillance, containment, and mitigation measures were implemented in the Federation of Bosnia and Herzegovina (FBiH). Our objective was to describe the surveillance methodology, response measures, and epidemiology of COVID-19 cases in FBiH from March 2020 to March 2022. The surveillance system implemented across FBiH enabled health authorities and the population to monitor the development of the epidemiological situation, the daily number of reported cases, as well as basic epidemiological characteristics and geographic distribution of cases. As of 31 March 2022, 249,495 cases of COVID-19, and a total of 8845 deaths were recorded in FBiH. Upkeeping of real-time surveillance, maintaining non-pharmaceutical interventions, and speeding up the vaccination roll-out were paramount for controlling COVID-19 in FBiH.

Abstract Background Immunomodulators and biologics are cornerstones in the management of inflammatory bowel disease [IBD], but are associated with increased risk of infections. Post-marketing surveillance registries are pivotal to assess this risk, yet mainly focus on severe infections. Data on the prevalence of mild and moderate infections are scarce. We developed and validated a remote monitoring tool for real-world assessment of infections in IBD patients. Methods A 7-item Patient-Reported Infections Questionnaire [PRIQ] covering 15 infection categories was developed with a 3-month recall period. Infection severity was defined as mild [self-limiting or topical treatment], moderate [oral antibiotics, antivirals, or antifungals], or severe [hospitalisation or intravenous treatment]. Comprehensiveness and comprehensibility were ascertained through cognitive interviewing of 36 IBD outpatients. After implementation in the telemedicine platform myIBDcoach, a prospective, multicentre cohort study was performed between June 2020 and June 2021 in 584 patients, to assess diagnostic accuracy. Events were cross-checked with general practitioner and pharmacy data [gold standard]. Agreement was evaluated using linear-weighted kappa with cluster-bootstrapping to account for within-patient level correlation. Results Patient understanding was good and interviews did not result in reduction of PRIQ items. During validation, 584 IBD patients {57.8% female, mean age 48.6 (standard deviaton [SD]: 14.8), disease duration 12.6 years [SD: 10.9]} completed 1386 periodic assessments, reporting 1626 events. Linear-weighted kappa for agreement between PRIQ and gold standard was 0.92 (95% confidence interval [CI] 0.89-0.94). Sensitivity and specificity for infection [yes/no] were 93.9% [95% CI 91.8-96.0] and 98.5% [95% CI 97.5-99.4], respectively. Conclusions The PRIQ is a valid and accurate remote monitoring tool to assess infections in IBD patients, providing means to personalise medicine based on adequate benefit-risk assessments.

Minarets, tall structures, connected or not to the mosque attract attention due to their specific architectural features. Vulnerability to seismic damage has been witnessed throughout history on tall and slender structures after earthquake ground motions. In that respect, it is of the utmost importance to investigate the dynamic characteristics and resilience of historical stone minarets. This paper aims to provide the results of an on-site dynamic investigation of a stone minaret in Mostar and deliver its seismic assessment. The minaret is part of the Tabačica mosque built at the turn of the 16th and 17th century in the City of Mostar, Bosnia and Herzegovina. The on-site investigation comprised dynamic identification of the minaret by ambient vibration testing and qualitative estimation of the masonry wall by sonic pulse velocity testing. Besides the modal analysis a time-history analysis was performed by using the Applied Element Method (AEM), considered an appropriate tool for assessing the behavior of historic masonry structures. A good match is found between the first natural frequency obtained by the on-site investigation and the modal analysis which is a solid basis for further seismic assessment of the minaret as a slender tower-like structure. The concentration of stresses is observed at the transition zones.

Abstract In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits.

The most significant bone used in taxonomic research is the skull. This study attempted to identify differences between the three cat species by measuring the skulls of each using computed tomography sections. The study used a total of 32 cat skulls, including 16 Van Cats, eight British Shorthairs and eight Scottish Folds. Van Cat had the highest values for cranial and skull length, whereas British Shorthair had the lowest values. The difference between the British Shorthair and Scottish Fold skull length and cranial length measures was not statistically significant. However, the Van Cat skull length result was statistically different from other species (p < 0.05). Scottish Fold had the broadest head (cranial width: 41.02 ± 0.79 mm). These results demonstrated that the scull of the Van Cat was longer but thinner than that of other species. In comparison to other species, the form of the Scottish Fold skull was more rounded. Internal height of cranium measurements for Van Cat and British Shorthair were statistically significant. In Van Cats, this measurement was 27.81 ± 1.58 mm, while in British Shorthairs, it was 30.23 ± 1.89 mm. Measurements of the foremen magnum were not statistically significant for any species. Van Cat's measures for the foramen magnum were the highest (Foramen magnum height: 11.59 ± 0.93 mm; Foramen magnum width: 14.18 ± 0.70 mm). Scottish Fold had the highest cranial index (Cranial index: 55.50 ± 4.02). Van Cat had the lowest value for this (Cranial index: 50.19 ± 2.16). Van Cat's cranial index value differed statistically from that of other species (p < 0.05). Between species, the foramen magnum index was not significant. None of the index values were statistically significant for Scottish Fold and British Shorthair. Foramen magnum width had the highest age‐to‐measurement value correlation (r = 0.310), although it was statistically insignificant. Skull length had the highest weight‐to‐measurement value correlations (R = 0.809), and it proved to be statistically significant. Skull length was the measuring value that distinguished male and female the most clearly (p = 0.000).

Restoring damaged microbiomes is still a formidable challenge. Classical widely adopted approaches consist of augmenting communities with pure or mixed cultures in the hope that these display their intended selected properties under in situ conditions. ABSTRACT Strain inoculation (bioaugmentation) is a potentially useful technology to provide microbiomes with new functionalities. However, there is limited understanding of the genetic factors contributing to successful establishment of inoculants. This work aimed to characterize the genes implicated in proliferation of the monoaromatic compound-degrading Pseudomonas veronii 1YdBTEX2 in nonsterile polluted soils. We generated two independent mutant libraries by random minitransposon-delivered marker insertion followed by deep sequencing (Tn-seq) with a total of 5.0 × 105 unique insertions. Libraries were grown in multiple successive cycles for up to 50 generations either in batch liquid medium or in two types of soil microcosms with different resident microbial content (sand or silt) in the presence of toluene. Analysis of gene insertion abundances at different time points (passed generations of metapopulation growth), in comparison to proportions at start and to in silico generated randomized insertion distributions, allowed to define ~800 essential genes common to both libraries and ~2,700 genes with conditional fitness effects in either liquid or soil (195 of which resulted in fitness gain). Conditional fitness genes largely overlapped among all growth conditions but affected approximately twice as many functions in liquid than in soil. This indicates soil to be a more promiscuous environment for mutant growth, probably because of additional nutrient availability. Commonly depleted genes covered a wide range of biological functions and metabolic pathways, such as inorganic ion transport, fatty acid metabolism, amino acid biosynthesis, or nucleotide and cofactor metabolism. Only sparse gene sets were uncovered whose insertion caused fitness decrease exclusive for soils, which were different between silt and sand. Despite detectable higher resident bacteria and potential protist predatory counts in silt, we were, therefore, unable to detect any immediately obvious candidate genes affecting P. veronii biological competitiveness. In contrast to liquid growth conditions, mutants inactivating flagella biosynthesis and motility consistently gained strong fitness advantage in soils and displayed higher growth rates than wild type. In conclusion, although many gene functions were found to be important for growth in soils, most of these are not specific as they affect growth in liquid minimal medium more in general. This indicates that P. veronii does not need major metabolic reprogramming for proliferation in soil with accessible carbon and generally favorable growth conditions. IMPORTANCE Restoring damaged microbiomes is still a formidable challenge. Classical widely adopted approaches consist of augmenting communities with pure or mixed cultures in the hope that these display their intended selected properties under in situ conditions. Ecological theory, however, dictates that introduction of a nonresident microbe is unlikely to lead to its successful proliferation in a foreign system such as a soil microbiome. In an effort to study this systematically, we used random transposon insertion scanning to identify genes and possibly, metabolic subsystems, that are crucial for growth and survival of a bacterial inoculant (Pseudomonas veronii) for targeted degradation of monoaromatic compounds in contaminated nonsterile soils. Our results indicate that although many gene functions are important for proliferation in soil, they are general factors for growth and not exclusive for soil. In other words, P. veronii is a generalist that is not a priori hindered by the soil for its proliferation and would make a good bioaugmentation candidate.

Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Naive Bayes with Logistic Regression. Materials and Methods: The Naive Bayes (N=10) and Logistic Regression Algorithm (N=10) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work. The sample size is determined using the G power Calculator and it’s found to be 10. Results: Based on the Results Accuracy obtained in terms of accuracy is identified by Naive Bayes (87.02%) over the Logistic Regression algorithm (92.18%). Statistical significance difference between novel Naive Bayes algorithm and Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.05). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Naive Bayes.

This study investigates the indirect mechanisms relevant to converting young adults' prosocial attitudes and individual responsibility into their prosocial behavior. Our results are based on a sample of 530 young adults studying at three public regional business schools in South East Europe. They show a significant favorable influence on young adults' civic and political involvement, mediating the relationship between individual responsibility attitudes and prosocial behavior. However, this would not have been expected based on previous research. Another indirect path between the same variables is modeled using a hypothesized moderated mediation effect. The institutional influence of higher education proves to be a significant mediator of the proposed relationship, moderated by the amount of educational content in the fields of ethics, social and environmental responsibility. At mid-and-high levels of exposure to relevant educational content, this indirect path significantly influences the developing young adults' pro-environmental behaviors. The study results are discussed from the viewpoint of peripheral regions with a history of dysfunctional social capital mechanisms.

Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.