Metagenomics-based gene exploration and biochemical characterization of novel glucoamylases and α-amylases in Daqu and Pu-erh tea microorganisms.

α-Amylases and glucoamylases play a crucial role in starch degradation for various industrial applications. Further exploration of novel α-amylases and glucoamylases with diverse enzymatic characteristics is necessary. In this study, metagenomics analysis revealed a high abundance of these enzymes in the microorganisms of Daqu and Pu-erh tea, identifying 271 glucoamylases and 232 α-amylases with significant sequence identity to known enzymes. Functional studies indicated that these enzymes have broad optimal temperatures (30 °C to 70 °C) and acidic or neutral pH optima. Additionally, two novel low-temperature glucoamylases and one novel low-temperature α-amylases were characterized, demonstrating potential for use in industries operating under low temperature conditions. Further analysis suggested that fewer molecular interactions and more flexible coli regions may contribute to their high activity at low temperatures. In summary, this study not only highlights the feasibility of exploring enzymes through metagenomic approaches, but also presents a library of novel and diverse α-amylases and glucoamylases for potential industrial applications.

Multi-step validation of a deep learning-based system with visual explanations for optical diagnosis of polyps with advanced features.

Artificial intelligence (AI) has been found to assist in optical differentiation of hyperplastic and adenomatous colorectal polyps. We investigated whether AI can improve the accuracy of endoscopists' optical diagnosis of polyps with advanced features. We introduced our AI system distinguishing polyps with advanced features with more than 0.870 of accuracy in the internal and external validation datasets. All 19 endoscopists with different levels showed significantly lower diagnostic accuracy (0.410-0.580) than the AI. Prospective randomized controlled study involving 120 endoscopists into optical diagnosis of polyps with advanced features with or without AI demonstration identified that AI improved endoscopists' proportion of polyps with advanced features correctly sent for histological examination (0.960 versus 0.840, p < 0.001), and the proportion of polyps without advanced features resected and discarded (0.490 versus 0.380, p = 0.007). We thus developed an AI technique that significantly increases the accuracy of colorectal polyps with advanced features.