Peptides released on frogs’ skin in a stress situation represent their only weapon against micro-organisms and predators. Every species and even population of frog possesses its own peptidome being appropriate for their habitat. Skin peptides are considered potential pharmaceuticals, while the whole peptidome may be treated as a taxonomic characteristic of each particular population. Continuing the studies on frog peptides, here we report the peptidome composition of the Central Slovenian agile frog Rana dalmatina population. The detection and top-down de novo sequencing of the corresponding peptides was conducted exclusively by tandem mass spectrometry without using any chemical derivatization procedures. Collision-induced dissociation (CID), higher energy collision-induced dissociation (HCD), electron transfer dissociation (ETD) and combined MS3 method EThcD with stepwise increase of HCD energy were used for that purpose. MS/MS revealed the whole sequence of the detected peptides including differentiation between isomeric Leu/Ile, and the sequence portion hidden in the disulfide cycle. The array of the discovered peptide families (brevinins 1 and 2, melittin-related peptides (MRPs), temporins and bradykinin-related peptides (BRPs)) is quite similar to that of R. temporaria. Since the genome of this frog remains unknown, the obtained results were compared with the recently published transcriptome of R. dalmatina.

In the pursuit of optimizing healthcare delivery and improving patient outcomes, the field of healthcare is undergoing a transformative shift from a reactive approach to a proactive one. This shift is facilitated by two upcoming technological developments: automation and artificial intelligence (AI). The abundance of data generated in the era of digital advancement presents both opportunities and challenges for healthcare. This paper explores the application of AI and machine learning in healthcare, focusing on the challenges posed by the exponential growth in data volume, the analysis of unstructured data, and the rapid pace of data refreshment. It examines the role of AI and machine learning in generating clinical decision support, uncovering disease subtypes and prognostic markers, and generating new hypotheses. In addition, this paper highlights the transformative potential of automation, AI, and robotics in healthcare, showcasing their ability to enhance efficiency, accuracy, and precision in patient care. By embracing these technological advancements, healthcare can achieve continuous progress in meeting the ever-growing demands and aspirations of the field while improving patient outcomes.

Medicinal plants are potentialsources of bioactive compounds.One of the medicinal plants used in the traditional medicine of Bosnia and Herzegovina isendemic Satureja subspicataL. In this work, we examined the ability of Satureja subspicataL. essential oil and hot water and methanol extractsto inhibit the enzymes acetylcholinesterase(AChE) and butyrylcholinesterase(BChE) using Ellman’s method.The ability ofSatureja subspicataL. essential oil in concentration of 1 mg/mL and 2 mg/mL to inhibit enzymes was moderate: 72.82%, and 76.89% for AChE, and 51.51%, and 27.15% for BChE, respectively. Analyzed hot water and methanol extractsin concentration of 1 mg/mL showed weak ability of cholinesterase inhibition. Extracts were additionally analyzed regardingtoability to protect proteins from oxidation, during 1 h and 24 h incubation periods. After incubation for 1 hhot water extractshowed a very good protective effect(10.61%), while the methanolic extract showed prooxidative activity. After incubation for 24 h, both extracts showed prooxidative activity.The obtained results show that the examined essential oil and extracts of S. subspicataL. containcompounds withcholinesterase inhibition and antioxidant potential, and thereforecan be useful in treatment of Alzheimer's disease.KEYWORDS:Satureja subspicata, essential oil, extracts, cholinesterase inhibition, protein oxidation

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.

Although many women perform postural tasks while listening to music, no study has investigated whether preferred music has different effects than non-preferred music. Thus, this study aimed to explore the effects of listening to preferred versus non-preferred music on postural balance among middle-aged women. Twenty-four women aged between 50 and 55 years were recruited for this study. To assess their static balance, a stabilometric platform was used, recording the mean center of pressure velocity (CoPVm), whereas the timed up and go test (TUGT) was used to assess their dynamic balance. The results showed that listening to their preferred music significantly decreased their CoPVm values (in the firm-surface/eyes-open (EO) condition: (p < 0.05; 95% CI [−0.01, 2.17])). In contrast, when the women were listening to non-preferred music, their CoPVm values significantly (p < 0.05) increased compared to the no-music condition in all the postural conditions except for the firm-surface/EO condition. In conclusion, listening to music has unique effects on postural performance, and these effects depend on the genre of music. Listening to preferred music improved both static and dynamic balance in middle-aged women, whereas listening to non-preferred music negatively affected these performances, even in challenged postural conditions.

The paper discusses figurative conceptualizations of nations, countries, and institutions as either a container, a person, a sinking ship, a fabric, or hell in media discourse on the European migrant crisis. Applying Steen et al.’s (2010) three-dimensional model of metaphor analysis, we analyze a specific set of metaphorical linguistic expressions, which are inextricably related in the segments of the real discourse on migration, to discuss their rhetorical power and communicative function. The aim of this paper is to describe and identify cases when these are used as perspective-changing devices to influence readers’ opinion on an important issue such as migration.