Thanks to the climatic and geographical conditions, the area of the Northwestern part of Bosnia and Herzegovina has a long tradition of producing honey and other bee products. However, there is little or no literature data on the physico-chemical properties and biological activity of different types of honey and other bee products from Bosnia and Herzegovina. Five different types of honey were analyzed: monofloral honey (acacia, chestnut, linden), meadow honey and forest honey. Physico-chemical parameters, sensory analysis, color of honey, antioxidant activity, and content of total phenols were analyzed in five types off collected honey samples. The analyzes performed showed that chestnut honey contains the highest and acacia honey has the lowest content oftotal phenolic compounds. The forest honey showed the best antioxidant activity. The color of the honey was measured according to the CIELab system and the estimated L, a, bparameters show that all types of honey from this area can be characterized asdark types of honey (L50) with the presence of a yellow color. The obtained results show that the analyzed samples of five different types of honey are rich in polyphenolic components and represent a good source of antioxidants in the human diet.KEYWORDS:honey,physico-chemical parameters, color, antioxidant activity, total phenols

Simple Summary Our study centers on refining the diagnosis of small-cell lung cancer (SCLC), a unique subtype with distinct therapeutic implications compared to other lung cancers. Our primary goal is the identification of specific differentially expressed proteins in SCLC as opposed to healthy lung tissue. Additionally, we aim to discern the protein expression of SCLC from large cell neuroendocrine carcinoma (LCNEC), a closely related entity. This research has the potential to enhance our understanding of these intricate lung cancers, potentially transforming the landscape of detection and tailored treatment strategies. Abstract The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

Simple Summary Merkel cell carcinoma (MCC) is a rare and highly aggressive type of skin neuroendocrine cancer that frequently recurs and metastasizes within a relatively short period. Despite rapid growth and characteristic skin color, MCC often goes undiagnosed in its early stage. Therefore, therapy is often initiated at the advanced stage, and selecting appropriate therapeutic interventions is critical. The emergence of novel immunotherapeutic agents, such as immune checkpoint inhibitors (ICI), presents a promising treatment option for advanced MCC. Several biomarkers, such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI), showed significant potential as predictive biomarkers for treatment with ICI. Despite their predictive value, each has demonstrated limited value in MCC over recent years. Abstract Merkel cell carcinoma (MCC) is primarily a disease of the elderly Caucasian, with most cases occurring in individuals over 50. Immune checkpoint inhibitors (ICI) treatment has shown promising results in MCC patients. Although ~34% of MCC patients are expected to exhibit at least one of the predictive biomarkers (PD-L1, high tumor mutational burden/TMB-H/, and microsatellite instability), their clinical significance in MCC is not fully understood. PD-L1 expression has been variably described in MCC, but its predictive value has not been established yet. Our literature survey indicates conflicting results regarding the predictive value of TMB in ICI therapy for MCC. Avelumab therapy has shown promising results in Merkel cell polyomavirus (MCPyV)-negative MCC patients with TMB-H, while pembrolizumab therapy has shown better response in patients with low TMB. A study evaluating neoadjuvant nivolumab therapy found no significant difference in treatment response between the tumor etiologies and TMB levels. In addition to ICI therapy, other treatments that induce apoptosis, such as milademetan, have demonstrated positive responses in MCPyV-positive MCC, with few somatic mutations and wild-type TP53. This review summarizes current knowledge and discusses emerging and potentially predictive biomarkers for MCC therapy with ICI.

Biflavonoids are dimeric forms of flavonoids that have recently gained importance as an effective new scaffold for drug discovery. In particular, 3′-8″-biflavones exhibit antiviral and antimicrobial activity and are promising molecules for the treatment of neurodegenerative and metabolic diseases as well as cancer therapies. In the present study, we directly compared 3′-8″-biflavones (amentoflavone, bilobetin, ginkgetin, isoginkgetin, and sciadopitysin) and their monomeric subunits (apigenin, genkwanin, and acacetin) and evaluated their radical scavenging activity (with DPPH), antifungal activity against mycotoxigenic fungi (Alternaria alternata, Aspergillus flavus, Aspergillus ochraceus, Fusarium graminearum, and Fusarium verticillioides), and inhibitory activity on enzymes (acetylcholinesterase, tyrosinase, α-amylase, and α-glucosidase). All the tested compounds showed weak radical scavenging activity, while antifungal activity strongly depended on the tested concentration and fungal species. Biflavonoids, especially ginkgetin and isoginkgetin, proved to be potent acetylcholinesterase inhibitors, whereas monomeric flavonoids showed higher tyrosinase inhibitory activity than the tested 3′-8″-biflavones. Amentoflavone proved to be a potent α-amylase and α-glucosidase inhibitor, and in general, 3′-8″-biflavones showed a stronger inhibitory potential on these enzymes than their monomeric subunits. Thus, we can conclude that 3′-8″-dimerization enhanced acetylcholinesterase, α-amylase, and α-glucosidase activities, but the activity also depends on the number of hydroxyl and methoxy groups in the structure of the compound.

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.