Artificial intelligence (AI) based on deep learning boosted medical research in the past years and is expected to enormously change the style of health care in many aspects in the foreseeable future. Nowadays, there are exploding volumes of healthcare-related data being generated daily. Because of its time-sensitive characteristics, being able to process large amounts of data in real-time fashion is crucial in healthcare settings. In gastroenterology practice, AI can manage and interpret the sheer amount of data with different formats coming from a myriad of sources, including currently used endoscopic or imaging devices, digital record systems, and electronic health records, or from other sources such as governmental databases, social media, or wearable devices over a long period. Traditional gastroenterology is nowadays beginning to transform to a new personalized, predictive, and preventive paradigm. Evidence-based practices and recent studies are coming out every day, and big data-based approaches and the progress in basic sciences and its emerging applications are now becoming the indispensable part of precision medicine. In gastroenterology, AI can be applied in disease diagnosis, treatment guidance, outcome prediction, and reducing workload of the healthcare staff. As the healthcare community begins to embrace AI technology, how to seamlessly construct an interoperable platform to accommodate data with high variety and veracity with high velocity and implement AI in the clinical workflow would be the future challenges.Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, including visual findings on endoscopy, radiologic imaging, manometric testing, genomes, proteomes, and metabolomes. However, clinical care is complex and requires a thoughtful approach to best deploy AI tools to improve quality of care and bring value to patients and providers. On the operational level, AI-assisted clinical management should consider logistic challenges in care delivery, data management, and algorithmic stewardship. There is still much work to be done on a broader societal level in developing ethical, regulatory, and reimbursement frameworks. A multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI-assisted tools to guide and enhance clinical practice. From optically enhanced endoscopy to clinical decision support for risk stratification, AI tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time.Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence.  https://www.selleckchem.com/products/dj4.html  The range of AI's ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.Glial cells play important roles in the development and homeostasis of metazoan nervous systems. However, while their involvement in the development and function in the central nervous system (CNS) of vertebrates is increasingly well understood, much less is known about invertebrate glia and the evolutionary history of glial cells more generally. An investigation into amphioxus glia is therefore timely, as this organism is the best living proxy for the last common ancestor of all chordates, and hence provides a window into the role of glial cell development and function at the transition of invertebrates and vertebrates. We report here our findings on amphioxus glia as characterized by molecular probes correlated with anatomical data at the transmission electron microscopy (TEM) level. The results show that amphioxus glial lineages express genes typical of vertebrate astroglia and radial glia, and that they segregate early in development, forming what appears to be a spatially separate cell proliferation zone positioned laterally, between the dorsal and ventral zones of neural cell proliferation. Our study provides strong evidence for the presence of vertebrate-type glial cells in amphioxus, while highlighting the role played by segregated progenitor cell pools in CNS development. There are implications also for our understanding of glial cells in a broader evolutionary context, and insights into patterns of precursor cell deployment in the chordate nerve cord.Isobavachalcone (IBC) has been shown to induce apoptosis and differentiation of acute myeloid leukemia (AML) cells. However, the underlying molecular mechanisms are not fully understood. Herein, IBC exhibited significant inhibition on the cell viability, proliferation, and the colony formation ability of AML cells. Moreover, IBC induced mitochondrial apoptosis evidenced by reduced mitochondrial membrane potential, increased Bax level, decreased Bcl-2, Bcl-xL, and Mcl-1 levels, elevated cytochrome c level in the cytosol and increased cleavage of caspase-9, caspase-3, and PARP. Furthermore, IBC obviously promoted the differentiation of AML cells, accompanied by the increase of the phosphorylation of MEK and ERK and the C/EBPα expression as well as the C/EBPβ LAP/LIP isoform ratio, which was significantly reversed by U0126, a specific inhibitor of MEK. Notably, IBC enhanced the intracellular ROS level. More importantly, IBC-induced apoptosis and differentiation of HL-60 cells were significantly mitigated by NAC.