63 results about "Muscle actin" patented technology

The invention provides a new medical application of the thiazolopyrone analog, that is, its application in the preparation of anti-hepatic fibrosis or anti-acute liver injury drugs. The general structural formula of the thiazolopyrone analogue is: Classical pharmacological experiments have confirmed that the thiazolopyrone analogue can down-regulate smooth muscle actin α-SMA and transforming growth factor TGF-SMA of human hepatic stellate cells LX-2 β1 expression, thereby inhibiting the proliferation and transformation of human hepatic stellate cells LX‑2. in CCl 4 In the induced mouse liver fibrosis / acute liver injury model experiment, thiazolopyrone analogs can inhibit the up-regulation of ALT and AST in mouse serum, and can inhibit the expression of α-SMA and TGF-β1 related to liver fibrosis , a variety of pathological section analysis and immunoomics experiments also confirmed that this analogue can effectively treat acute liver injury and liver fibrosis in mice. Therefore, this type of thiazolopyrone compound can be used as an active substance for the preparation of anti-hepatic fibrosis or anti-acute liver injury drugs.

A method and system for predicting liver injury in vivo due to hepatocyte damage by a test compound are provided. The method includes acquiring images of fluorescently stained cells obtained from a cell culture in which the cells have been treated with a dose-range of at least the test compound and its vehicle. The cells may be hepatic cells including primary or immortalized hepatocytes, hepatoma cells or induced pluripotent stem cell-derived hepatocyte-like cells. The acquired images are segmented. The method further includes extracting and analyzing one or more phenotypic features from the segmented images, wherein the one or more phenotypic features are selected from the group of intensity, textural, morphological, or ratiometric features consisting of (a) features of DNA, (b) features of RELA (NF-KB p65), and (c) features of actin filaments at different subcellular regions and d) features of cellular organelles and their substructures in the segmented images. Finally, the method includes normalizing results from the treated samples to vehicle controls and predicting the probability of liver injury by the test compound based on test compound-induced normalized changes of the extracted and selected phenotypic features using machine learning methods.