Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches

Walnut protein isolate based emulsion as a promising delivery system enhanced lutein bioaccessibility

Lutein, a natural pigment with multiple beneficial bioactivities, faces limitations in food processing due to its instability. In this study, we constructed four modified walnut protein isolate (WNPI) based emulsions as emulsion-based delivery systems (EBDS) for lutein fortification. The modification treatments enhanced the encapsulation efficiency of the WNPI-based EBDS on lutein. The modified WNPI-based EBDS exhibited improved storage and digestive stability, as well as increased lutein delivery capability in simulated gastrointestinal conditions. After in vitro digestion, the lutein retention in the modified WNPI-based EBDS was higher than in the untreated WNPI-based EBDS, with a maximum retention of 49.67 ± 1.10 % achieved after ultrasonic modification. Furthermore, the modified WNPI-based EBDS exhibited an elevated lutein bioaccessibility, reaching a maximum value of 40.49 ± 1.29 % after ultrasonic modification, nearly twice as high as the untreated WNPI-based EBDS. Molecular docking analysis indicated a robust affinity between WNPI and lutein, involving hydrogen bonds and hydrophobic interactions. Collectively, this study broadens WNPI's application and provides a foundation for fortifying other fat-soluble bioactive substances.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.