Non-tuberculous mycobacteria (NTM) are opportunistic pathogens capable of causing infections in humans and animals. The aim of this study was to demonstrate the potential role of domestic and wild animals as a reservoir of multiple resistant, rapidly growing NTM strains representing a potential zoonotic threat to humans. A total of 87 animal isolates belonging to 11 rapidly growing species (visible colonies appear within three to seven days) were genotyped and tested for susceptibility to the 15 most commonly used antibiotics in the treatment of such infections in a human clinic. By determining the antimicrobial susceptibility, the most prevalent resistance was found to cephalosporins (>50%), followed by amoxicillin–clavulanate (31.0%), clarithromycin (23.0%), tobramycin (14.9%) and doxycycline (10.3%). Resistance to imipenem, ciprofloxacin, minocycline and linezolid was notably lower (<7.0%). All tested isolates were susceptible to amikacin and moxifloxacin. The most frequent resistance was proved in the most pathogenic species: M. fortuitum, M. neoaurum, M. vaccae and M. porcinum. Meanwhile, other species displayed a higher sensitivity rate. No significant resistance differences between domestic and wild animals were found. The established significant frequency of resistance highlights the significant zoonotic potential posed by circulating rapidly growing NTM strains, which could lead to challenges in the treatment of these infections.

Industrial production is currently experiencing a revolutionary transformation through digitization processes and networked technology, so that it necessarily goes through a series of essential changes, which require conceptual design and creation of new terms. In the field of social organization of work, the processes of management and control are experiencing radical changes, and many novelties that bring technological innovations provide the opportunity to analytically observe the interaction relationships of different systems, without losing individuals, organizations and society in the context. Given that digital transformation is not a uniform process, the paper presents the advantages of a holistic approach in the analysis of organizational changes. New ways of organizing work bring various kinds of challenges that must be understood in order to be able to detect the social mechanisms that are at the very basis of change. The paper points out the specific social dimensions of technology that appear during the creation of organizational processes within the framework of the Fourth Industrial Revolution. The very term ‘industrial revolution’ is understood in the paper in a broader sense, and includes changes in social relations and the status of certain social groups, and not only changes in the production process and factors of production. The paper explains the status changes in power that are connected with the possibility of making business decisions in various forms of organizational practices, showing the increasingly pronounced complexity of interactional relationships between people and technology, which points to the necessity of interdisciplinary observation and finding a holistic approach to understanding the nature of the changes that are taking place.

Business operations of companies in modern conditions are subject to enormous market, social and especially technological pressures from the environment. Information and communication technologies have become so incorporated both in our everyday life and in the operations of every company, that without them we feel almost lost and helpless. Big Data, as a theoretical (philosophical) concept has existed for decades, but only recently, thanks to the extraordinarily rapid development of information and communication technologies, it has become applicable in practice, and as a business concept it has been recognized as a unique opportunity for success in the business world. Like all organizations, small and medium-sized enterprises can find a unique opportunity to improve their own business in the application of this concept. The number of users is growing exponentially, generating a huge amount of different data every second through different sources (YouTube, Twitter, Instagram, Facebook, Google, Skype, Internet, E-mail). All those unimaginably large amounts of data need to be stored somewhere: processed, analyzed, presented and interpreted, and then propose (suggest) specific business solutions based on those results. Realization of those activities in real or reasonable time, and often unexpected and surprising conclusions, are made possible by the Big Data concept. This article aims to shed light on the concept and technology of Big Data and its application at the level of small and medium enterprises. Big Data is a theoretical and technological concept, which is able to revolutionize the way of decision-making in companies and achieve extraordinary and concrete results. A secondary, but no less important, goal of writing this paper is to point out the importance of small and medium-sized enterprises, which outnumber the large ones. Most of them strive for a stable, dominant and high market position, so it can be concluded that they are extremely important for development and progress of each country.

Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech processing can be investigated by measuring their electrical activity using scalp-mounted electrodes. However, typical analysis methods involve averaging neural responses to many short repetitive stimuli that bear little relevance to daily listening environments. Recently, subcortical responses to more ecologically relevant continuous speech were detected using linear encoding models. These methods estimate the temporal response function (TRF), which is a regression model that minimises the error between the measured neural signal and a predictor derived from the stimulus. Using predictors that model the highly non-linear peripheral auditory system may improve linear TRF estimation accuracy and peak detection. Here, we compare predictors from both simple and complex peripheral auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 participants listening to continuous speech. We also discuss the data length required for estimating subcortical TRFs with clear peaks. Interestingly, predictors from simple models resulted in TRFs that were similar to those estimated using complex models, and were much faster to compute. This work paves the way for efficient modelling and detection of subcortical processing of continuous speech, which may lead to improved diagnosis metrics for hearing impairment and assistive hearing technology.

Tussilago farfara L., also known as coltsfoot, is a plant that has been used since ancient times to relieve coughs. Subsequently, the effectiveness of coltsfoot in the treatment of bronchial asthma, pneumonia and other respiratory diseases was established. In this research, the antioxidant and antimicrobial activity of different coltsfoot extracts was analyzed. The antioxidant activity was monitored through the DPPH radical inhibition efficiency and the extract's reducing ability. Antibacterial activity was tested using the diffusion technique. Tussilago farfara L. extracts showed extremely high antioxidant activity in in vitro conditions. The highest antioxidant capacity was found in extracts prepared by mixing water and organic solvents. High antibacterial activity was found for ethanolic, acetone and aqueous-ethanolic extracts of coltsfoot.

Introduction: The need for this study on the prevalence of hypodontia in a sample of orthodontic patients from Bosnia and Herzegovina arises from the fact that in the state of Bosnia and Herzegovina, there is a lack of data on the prevalence and distribution of hypodontia as a previously defined, growing global problem and that it could emphasize the importance of its early diagnosis, as this is still the best standard for preventing the occurrence of complications due to a lack of teeth. The aim of this retrospective study was to determine the prevalence and distribution of hypodontia in orthodontic patients in Bosnia and Herzegovina. Methods: The study was conducted in the Orthodontic Department of the University of Sarajevo, Faculty of Dentistry, with the Center for Clinical Dentistry. The study included all patients who presented to the Orthodontic Department in the period from January 2020 to March 2023 and whose diagnostic protocol included panoramic radiographs, which were analyzed for the purposes of this study. The sample consisted of the orthopantomography radiographs of 5084 patients (2031 males and 3053 females). Results: Hypodontia was found in 6.77% of orthodontic patients, of which 38.4% were male patients and 61.6% were female. The teeth most frequently affected by hypodontia were the second premolars (68.15%). After stratifying the sample by jaw, the most common hypodontic tooth was the second premolar in the mandible and the lateral incisor in the maxilla. Conclusion: We can conclude that the prevalence of hypodontia in orthodontic patients in Bosnia and Herzegovina and its distribution are very similar to the general population. The results of this study may help to improve the early diagnosis of hypodontia as a growing problem, which may reduce the complications that may arise from missing a tooth.

Introduction: Insulin resistance (IR) is a complex pathophysiological condition multifactorial etiology characterized by diminished responsiveness of insulin target tissues. Today, various diagnostic approaches involving different laboratory parameters are available, but simple and non-invasive indices based on mathematical models are increasingly used in practice. This study aims to assess the effectiveness of various clinical surrogate indices in predicting IR across a population with varying body weights. Methods: The matched case-control study was conducted between January 2021 and December 2022. Secondary data extracted from the medical records of 129 subjects was analyzed, including demographic characteristics (age and gender), anthropometric measures (height and weight), and biochemical laboratory test results. y further divided into two subgroups based on body mass index (BMI): overweight (BMI between 25 and 29.9 kg/m2) and obese (BMI of 30 kg/m2 or higher). Using laboratory data values for six widely used clinical surrogate markers were calculated: Homeostatic model assessment for IR (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), Mcauley index (MCAi), metabolic score for IR (METS-IR), Triglyceride to Glucose Index (TyG), and TyG to BMI (TyG-BMI). Results: Significant differences in HOMA-IR levels were observed between the groups (p < 0.001). A similar pattern was found for the TyG-BMI, with notable differences (p < 0.001). The obese participants had the highest mean levels for METS-IR and the TyG index while the control group had the highest mean values for the QUICKI and MCAi indices (p < 0.001). According to the analysis, three indices showed statistical significance in predicting IR independent of BMI (p < 0.05). Sensitivity and specificity were higher in the obese group (0.704 and 0.891) than in the overweight group (0.631 and 0.721). Conclusion: Given that IR is a multifactorial disease, using derived indices based on a combination of biochemical parameters and anthropometric indicators can significantly aid in predicting and mitigating numerous complications.