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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

The identification of coronary artery diseases has been greatly helped by computed tomography (CT) images of the coronary angiography. The patient is exposed to significantly more radiation during this radiological test than during previous similar ones. The purpose of this study was to evaluate the effective dosage for computed tomography (CT) coronary angiography and optimise the radiation dose. 380 patients with coronary artery abnormalities were referred to the Primus Diagnostic Centre and Health City Hospital in Guwahati, Assam, throughout the research period. Data on the technical aspects of CT procedures were collected in 2022. Our study's aim was to estimate the organ and surface dose to a single radiosensitive organ (the chest) using the programme imPACT 1.0.4 and the SR250 Monte Carlo dataset from the National Radiological Protection Board (NRPB). The study's 380 subjects, who ranged in age from 29 to 75, comprised 190 men and 190 women. BMI and ED had corresponding Mean SD values of 22.42±1.06 and 21.57±4.27. The mean ED is 21.57 and the mean DLP is 854.67. Males (13-27) and females (13-29) received the same effective dosages of mSv. Because other nations have previously started with more advanced CT procedures including dosages for paediatrics, coronary angiography, and CT fluoroscopy, this study is truly a pioneer in presenting fundamental data of doses of CT examinations in Assam. With this study, there may be more opportunities to create complex new studies or enhance the data from related studies that may be done in future work. To achieve high precision with minimum risk, the current study can be considered as need of the our