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Enzymes are essential biocatalysts involved in all biochemical and metabolic reactions, widely used across industries, especially in food processing. Historically utilized to enhance food production, these enzymes aid in breaking down food for better digestion while improving taste, texture, and aroma. They are derived from animals, plants, or microorganisms, with microbial sources being the most preferred due to their cost-effectiveness, stability, ease of cultivation, and potential for large-scale production. Advances in biotechnology, molecular biology, and enzyme engineering have significantly deepened our understanding of microbial enzymes and enhanced their applications in the food industry. The integration of recombinant DNA technology and process engineering has further optimized enzyme-producing microbes for industrial use. However, continued research is essential to address challenges and fully harness their potential. This review focuses on microbial enzyme sources, production techniques, strain improvement methods, and their diverse applications in food processing. Graphical Abstract

Background Diabetes, a chronic metabolic disorder characterized by ineffective blood sugar regulation, affects millions of people worldwide, with its prevalence projected to more than double in the next 30 years. Diabetes-related complications are severe and sometimes life-threatening, including cardiovascular disease, kidney failure, and blindness, this posing a significant challenge, especially in low- and middle-income countries. This study explored the integration of artificial intelligence (AI) into diabetes management, emphasizing its transformative potential in healthcare. Objectives To evaluate the role of AI in enhancing diabetes management and to identify the challenges and opportunities associated with its implementation. Methods A systematic review following the PRISMA guidelines was conducted by analyzing the literature published from January 2020 to May 2024. This review focused on the application of AI in diabetes diagnosis, personalization of treatment, and predictive analytics. Results The ability of AI to analyze large datasets and identify complex patterns shows promise in improving diabetes management. AI-assisted diagnostic tools enhance diagnostic accuracy, enable early detection, and support personalized treatment plans, thereby reducing human error. AI has also facilitated research breakthroughs in genomics and drug discovery. Furthermore, AI-powered predictive analytics enhances clinical decision-making and supports precision medicine. Despite these advancements, challenges remain in such issues as data quality, technical infrastructure, and ethical considerations, emphasizing the need for responsible AI development that focuses on patient privacy and transparency. Conclusions AI has significant potential to revolutionize diabetes management and healthcare delivery. Combining AI’s analytical processes with clinical expertise can substantially improve the quality of care. Addressing data, technology, and ethical challenges is crucial for fully harnessing AI’s potential, thereby enhancing patient well-being and healthcare outcomes. Graphical Abstract