Background & Aims Biliary epithelial cells (BECs) are considered to be a source of regenerating hepatocytes when hepatocyte proliferation is compromised. However, there is still controversy about the extent to which BECs can contribute to the regenerating hepatocyte population, and thereby to liver recovery. To investigate this issue, we established a zebrafish model of liver regeneration in which the extent of hepatocyte ablation can be controlled. Methods Hepatocytes were depleted by administration of metronidazole to Tg(fabp10a:CFP-NTR) animals. We traced the origin of regenerating hepatocytes using short-term lineage tracing experiments as well as the inducible Cre/loxP system; specifically, we utilized both a BEC tracer lineTg(Tp1:CreERT2) and a hepatocyte tracer line Tg(fabp10a:CreERT2). We also examined BEC and hepatocyte proliferation as well as liver marker gene expression during liver regeneration. Results BECs gave rise to most of the regenerating hepatocytes in larval and adult zebrafish after severe hepatocyte depletion. Following hepatocyte loss, BECs proliferated as they dedifferentiated into hepatoblast-like cells; they subsequently differentiated into highly proliferative hepatocytes that restored the liver mass. This process was impaired in zebrafish wnt2bb mutants; in these animals, hepatocytes regenerated but their proliferation was greatly reduced. Conclusions BECs contribute to regenerating hepatocytes following substantial hepatocyte depletion in zebrafish, thereby leading to recovery from severe liver damage.

Although many hypo-pigmenting agents are currently available, the demand for novel whitening agents is increasing, in part due to the weak effectiveness and unwanted side effects of currently available compounds. To screen for novel hypo-pigmenting agents, many methodologies such as cell culture and enzymatic assays are routinely used. However, these models have disadvantages in terms of physiological and economic relevance. In this study, we validated zebrafish as a whole-animal model for phenotype-based screening of melanogenic inhibitors or stimulators. We used both the well-known melanogenic inhibitors (1-phenyl-2-thiourea, arbutin, kojic acid, 2-mercaptobenzothiazole) and newly developed small molecule compounds (haginin, YT16i). All the tested compounds produced inhibitory effects on the pigmentation of zebrafish, most likely due to their inhibitory potential on tyrosinase activity. In simultaneous in vivo toxicity tests, a newly developed melanogenic inhibitor YT16i showed massive abnormalities in terms of deformed morphologies and cardiac function. Together, these results provide a rationale in screening and evaluating the putative melanogenic regulatory compounds. We suggest that the zebrafish system is a novel alternative to mammalian models, with several advantages including the rapidity, cost-effectiveness, and physiological relevance.

Zebrafish have several advantages compared to other vertebrate models used in modeling human diseases, particularly for large-scale genetic mutant and therapeutic compound screenings, and other biomedical research applications. With the impactful developments of CRISPR and next-generation sequencing technology, disease modeling in zebrafish is accelerating the understanding of the molecular mechanisms of human genetic diseases. These efforts are fundamental for the future of precision medicine because they provide new diagnostic and therapeutic solutions. This review focuses on zebrafish disease models for biomedical research, mainly in developmental disorders, mental disorders, and metabolic diseases.

Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.