This study investigated various chemical and antioxidant properties of Anatolian royal jelly samples. Moisture, pH, total protein, 10-hydroxy-2-decenoic acid (10-HDA) and sugars were analyzed from 18 samples. Total phenolic contents, ferric reducing antioxidant capacity and 2,2-diphenyl-1-picryhydrazyl (DPPH) free radical scavenging activity were measured as antioxidant determinants. 10-HDA contents and total protein content of fresh weight ranged between 1.0% and 3.9%, and 11.4% and 15.8%, respectively. The main sugars detected were glucose and fructose. Maltose, trehalose, and melibiose were detected at less than 1.0% in all samples. Lactose, a milk sugar, was detected in only 3 samples, at values between 0.8% and 1.4%. Total henolic content ranged from 91.0 to 301.0 mg gallic acid equivalents/kg fresh weight. Antioxidant activity is due to both to the total phenolic content, proteins and fatty acids of royal jelly. Anatolian royal jelly samples were not different from other royal jelly samples from across the world.

Introduction: Augmented reality (AR) technology is rapidly evolving and is finding an increasing application in education, including medical training. This feasibility study aimed to explore the usability and didactic potential of AR with the HoloLens2™ for medical students, teaching the primary survey of burn wounds. Methods: This feasibility study was conducted using a prospective observational cohort design. Test groups consisted of participants with limited (n = 18), moderate (n = 10), or high (n = 5) experience in burn care, and f illed in a questionnaire after training with the HoloLens2™. Results: Outcomes of the questionnaires show AR to be a promising technology for educating medical students in the primary survey of burn wounds. Discussion: However promising, there is further need for development in usability and image quality. The ability to simulate realistic scenarios in a safe and scalable environment could pave the way for a new era for medical education, where AR becomes a valuable supplement or even replacement for traditional learning methods.

The integration of artificial intelligence (AI) with medical imaging tools has enabled faster and more accurate diagnostic processes, transforming radiology into a more precise, efficient, and data-driven medical discipline. However, the successful implementation of AI-based medical imaging tools in emotionally sensitive, life-critical domains such as radiology depends heavily on public trust and acceptance. This study examines how value perceptions and trust shape behavioral intentions to adopt AI-based tools in radiology by extending Esmaeilzadeh's Value-Based Model, which is conceptually aligned with Privacy Calculus Theory. To enhance the model's explanatory power, additional variables were incorporated, including perceived knowledge as a predictor and trust as a mediating factor. A cross-sectional online survey (N = 961) was conducted, and data was analyzed through structural equation modeling. The findings indicate that perceived risk, perceived benefit, and perceived knowledge significantly influence trust perception. Importantly, trust served as a key mediating variable, partially mediating the effects of these factors on the intention to use AI-based medical imaging tools. The inclusion of trust increased the model's explanatory power from R 2 = 0.68 to R 2 = 0.74. Multigroup analysis based on gender, age, and education level revealed significant differences in certain pathways; however, the effect sizes were small. These f indings highlight the importance of developing inclusive and targeted strategies that address both technical and emotional concerns, enhance perceived benefits, foster public trust, and strengthen the intention to use AI-based tools in radiology.