Atatürk University

Sarcopenia is a chronic disease characterized by loss of skeletal muscle mass and strength, posing a rising societal health challenge among the elderly, leading to increased risks of disability, fractures, falls, and mortality. Despite its clinical significance, there are currently no approved medicinal interventions for this condition. In this study, we used a systems biology framework to uncover potential therapeutic targets for sarcopenia. Based on computational analysis, CYC1 was identified as a key druggable protein. Following bioinformatic screening, ML167 was selected as a candidate for repurposing. Guided by this insight, we have rationally designed and synthesized a novel series of ML-167-based derivatives, intending to modulate CYC1 activity. For structural validations, comprehensive spectroscopic techniques, including 1H and 13C NMR, MS, and, where applicable, singlecrystal X-ray crystallography techniques were used. In vitro evaluation of the ML-167 derivatives in C2C12 cells established structure-dependent effects on cell viability. Several derivatives exhibited proliferation levels similar to the control group and indicate differences in biological activity among the derivatives. Computational studies, including molecular docking, density functional theory (DFT) calculations, and analyses of frontier molecular orbitals (FMOs), disclosed favorable electronic features, charge-transfer characteristics, and binding interactions with CYC1. Noncovalent interaction (NCI), reduced density gradient (RDG), electron localization function (ELF), and quantum theory of atoms in molecules (QTAIM) analyses highlighted the role of dispersive forces and hydrogen bonding in stabilizing the ligand-protein complex. Structure-activity relationship (SAR) analysis and in vitro cell proliferation assays identified compounds 3a, 3f, and 5a as the most promising leads, demonstrating optimal binding profiles and favorable biological activity.

This study investigated the phytochemical composition and biological potential of the methanol extract of the aboveground parts of Thymus leucotrichus var. leucotrichus (METL), a high-altitude Thymus taxon used as a spice and medicinal plant. METL was evaluated for antioxidant activity (Fe3+, Cu2+, and Fe3+-TPTZ reduction; DPPH, ABTS, and DMPD radical scavenging assays), antimicrobial activity, and its inhibitory effects on α-glycosidase (α-Gly), acetylcholinesterase (AChE), carbonic anhydrase I and II (CA I and II), total phenolic and flavonoid content, phenolic compound analysis via UPLC-MS/MS, and the binding effect of the major active compound, rosmarinic acid, on enzymes. At 20 μg/mL, METL showed absorbances of 0.718, 0.856, and 1.152 in the Fe3+, Cu2+, and Fe3+-TPTZ reduction assays, respectively. The IC50 values for DPPH, ABTS, and DMPD radical scavenging were 20.39, 19.16, and 94.53 μg/mL, respectively. The IC50 values for METL’s inhibitory effects on AChE, α-Gly, and CA I and II enzymes are 3.40 μg/mL, 9.2 μg/mL, 11.50 μg/mL, and 12.9 μg/mL, respectively. The total phenolic content of METL is 186.66 mg/g GAE, and the total flavonoid content is 161.94 mg/g QE. UPLC-MS/MS analysis of METL identified rosmarinic acid (RA) at 17,961.65 μg/g, cinnamic acid at 2,189.88 μg/g, and chlorogenic acid at 281.16 μg/g as the major components. The correlation between RA and enzymes was then investigated using molecular docking analysis. The antimicrobial activity of METL was tested against a total of 12 isolates, comprising 8 standard strains and 4 hospital strains. METL exhibited a minimum inhibitory concentration (MIC) of 0.5 mg/mL against Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, colistin-resistant K. pneumoniae, Bacillus cereus, Proteus mirabilis, and Candida albicans. Overall, the findings demonstrate that METL combines marked antioxidant capacity, antimicrobial activity, and inhibitory effects on AChE, α-Gly, and CA I-II, with RA as its predominant quantified phenolic constituent.

Aim This study aims to determine peer bullying behaviors and evaluate the effect of dramatic play in reducing these behaviors in preschool children. Materials and methods This study used a pretest-posttest quasi-experimental design and involved 45 children at Bitlis Tatvan İbn-i Sina Kindergarten. Data were collected through the “Descriptive Information Form”, the “Preschool Peer Bullying Scale - Teacher Form”, and the “Bully and Victim Child Behaviors Assessment Form”. The study included an intervention program lasting over 10 weeks and consisting of video animation presentations, implementation of five dramatic play scenarios, and reinforcement activities. Data were analyzed using SPSS 23.0 and the Shapiro-Wilk normality test, skewness and kurtosis values, percentages, means, and paired-samples t-test analyses. Results Study findings showed that after the dramatic play intervention, children's peer bullying behaviors decreased significantly in the posttest compared to the pretest (p < 0.001). In addition, statistically significant decreases were observed in the bullying, victim, and passive bullying child behaviors subscales in the posttest compared to the pretest (p < 0.001). Conclusion This study showed that a nursing intervention based on dramatic play administered in the preschool period was effective in reducing children's peer bullying behaviors and related subscales. The results underscore the importance of play-based interventions in early childhood for combating bullying.

Abiotic stresses, including drought, salinity, heat, and nutrient imbalances, severely constrain cereal crop productivity and pose a growing threat to global food security under climate change. While traditional breeding has contributed to crop improvement, its limited speed and resolution necessitate the integration of advanced biotechnological approaches. Recent developments in genome editing, particularly CRISPR/Cas systems, alongside marker-assisted selection, genomic selection, and multi-omics technologies, have enabled precise manipulation of stress-responsive genes and accelerated trait discovery in major cereals such as wheat, rice, and maize. This review synthesises current advances in the physiological, molecular, and genomic mechanisms underlying abiotic stress tolerance, with a particular emphasis on integrative frameworks that combine genomics, phenomics, and computational approaches. Importantly, emerging constraints associated with genome editing, including off-target effects, delivery challenges, and mosaicism, highlight the need for complementary strategies. The integration of pangenomics, high-throughput phenotyping, artificial intelligence-assisted selection, and speed breeding represents a transformative shift toward systems-level crop improvement. Overall, this review proposes that future progress in developing climate-resilient cereals will depend not on individual technologies alone, but on their coordinated deployment within holistic, data-driven breeding pipelines capable of addressing complex and dynamic stress environments.

BackgroundAccurate modelling of airflow and aerosol/particle dynamics within the human respiratory system is essential for improving inhalation-based drug delivery strategies and for evaluating the health risks associated with hazardous particulates. Owing to the complex geometry of the human airways, inter-individual anatomical variations, and variable breathing patterns, this process constitutes a highly complex multiphase flow problem. To address the constraints associated with in vivo and in vitro techniques, in silico approaches based on computational fluid dynamics (CFD) have been extensively utilized to examine respiratory airflow and aerosol dynamics at microscopic scales.ObjectivesThe aim of this study is to review recent CFD-based approaches for modeling airflow and aerosol behavior in the human respiratory system, summarize key modeling strategies and influential parameters, and identify future research directions.ResultsRecent studies indicate a transition of respiratory tract models toward more physiologically realistic and whole-lung representations. These studies demonstrate that coupling CFD with particle models enables reliable prediction of aerosol transport and deposition by accounting for the effects of geometric variations, breathing conditions, turbulence characteristics, and particle physical and chemical properties.ConclusionCFD-based modeling, particularly when integrated with particle dynamics, provides a powerful and reliable framework for investigating airflow and aerosol behavior in the human respiratory system. Continued advancements toward realistic whole-lung models and improved representation of physiological and particle-related parameters are expected to further enhance predictive accuracy and support both clinical and environmental health applications.