Omics analysis of key pathway in flavour formation and B vitamins synthesis during chickpea milk fermentation by Lactiplantibacillus plantarum.

Chickpea milk is a nutrient-rich plant-based milk, but its pronounced beany flavour limits consumer acceptance. To address this issue, chickpea milk was fermented using two strains of Lactiplantibacillus plantarum, FMBL L23251 and L23252, which efficiently utilize chickpea milk. L. plantarum FMBL L23251 demonstrated superior fermentation characteristics. Fermentation with L. plantarum FMBL L23251 resulted in a 1.90-fold increase in vitamin B3 (271.66 ng/ml to 516.15 ng/ml) and a 1.58-fold increase in vitamin B6 (91.24 ng/ml to 144.16 ng/ml) through the L-aspartic acid pathway and the 1-deoxy-D-xylulose-5-phosphate (DXP)-independent pathway, respectively. Furthermore, L. plantarum FMBL L23251 effectively removed beany flavours due to its enhanced pathway for pyruvate metabolism. The main aldehydes are converted into corresponding alcohols or acids, resulting in 87.74 % and 96.99 % reductions in hexanal and 2-pentyl-furan, respectively. In summary, the fermentation of L. plantarum FMBL L23251 generated fermented chickpea milk that is rich in B vitamins and provides a better flavour.

Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology.

Background: Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research. Methods: This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research. Results: Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly. Conclusions: The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.