The gradient of the Casimir force between a Si-SiO 2 -graphene substrate and an Au-coated sphere is measured by means of a dynamic atomic force microscope operated in the frequency shift technique. It is shown that the presence of graphene leads to up to 9% increase in the force gradient at the shortest separation considered. This is in qualitative agreement with the predictions of Lifshitz theory using the dielectric permittivities of Si and SiO 2 and the Dirac model of graphene.

This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.

Combinatorial organic synthesis (combinatorial chemistry or CC) and ultrahigh-throughput screening (UHTS) are speeding up drug discovery by increasing capacity for making and screening large numbers of compounds. However, a key problem is to select the smaller set of "representative" compounds from a virtual library to make or screen. Our approach is to select drug-like as well as structurally diverse compounds. The compounds, which are not very drug-like, are less taken into account or excluded even if they contribute to the diversity of the collection. Hence, the first step in the compound selection is to rank compounds in drug-like "degree". To quantify the drug-like "degree", drug-like index (DLI) is introduced in this paper. A compound's DLI is calculated based upon the knowledge derived from known drugs selected from Comprehensive Medicinal Chemistry (CMC) database. The paper describes the way of this knowledge base is formed and the procedure for selecting drug-like compounds.

E2F transcription factor 1 (E2F1) is an important regulator of metabolic diseases, however its role in liver function remains elusive. This study unraveled a regulatory cascade involving E2F1, early growth response-1 (Egr-1), nuclear receptor small heterodimer partner (SHP, NR0B2), and EIA-like inhibitor of differentiation 1 (EID1) in cholestatic liver fibrosis. Liver E2F1 mRNA and protein expression was strongly upregulated in human nonalcoholic steatohepatitis (NASH) and alcohol cirrhosis; the latter was inversely correlated with diminished SHP expression. E2F1 was also highly induced by 3, 5- diethoxycarbonyl-1, 4-dihydrocollidine (DDC) feeding and bile-duct ligation (BDL) in mice. E2F1−/− mice exhibited reduced biliary fibrosis by DDC as determined by Masson Trichrome and Picro Sirius red staining, and decreased serum bile acid (BA), BA pool size, and fecal BA excretion. In addition, cholestatic liver fibrosis induced by BDL, as determined by immunohistochemistry analysis of a1 collagen expression, was increased in SHP−/− mice but attenuated in hepatocyte SHP-overexpressed transgenic (STG) mice. Egr-1 exhibited marked induction in livers of SHP−/− mice compared to the wild type mice in both sham and BDL groups, and reduction in STG livers. Egr-1 promoter was activated by E2F1, and the activation was abrogated by expression of SHP and its co-repressor EID1 in hepatoma cells Huh7, Hepa1, and stellate cells LX2. ChIP assays further confirmed the association of E2F1, SHP and EID1 proteins with the Egr-1 promoter, and their direct protein interactions were determined by GST pull-down assays. Interestingly, E2F1 activated Egr-1 expression in a biphasic fashion as described in both human and mouse hepatocytes. Conclusion E2F1 is a fibrogenic gene and could serve as a potential new diagnostic marker for non-alcoholic and alcoholic liver fibrosis/cirrhosis.