Human fibroblast growth factor 19 (FGF19), its receptor (FGFR4) and EpCAM play an important role in cell proliferation, differentiation, motility, and overexpression have been linked to hepatocellular carcinoma (HCC). The aim of this study was to evaluate the FGF19 signals responsible for the progression of HCC arising from fatty liver.FGF19 level was significantly increased in the HCC patients' serum compared to non-HCC controls. The IHC results demonstrated significant increases of protein expressions of FGF19, FGFR4 and EpCAM in specimens with fatty liver, NASH, cirrhosis, and HCC compared to healthy liver tissue. There was a significant positive correlation between the protein expressions (FGF19, FGFR4, and EpCAM) and histopathologic changes from FL to HCC. Furthermore, FGF19 was positively correlated with FGFR4 and with EpCAM.FGF19 protein levels in serum and tissues were determined by ELISA assay. The FGFR4, and EpCAM expression and tissue distribution were further evaluated by immunohistochemical staining in tissue array samples. FGF19, FGFR4 and EpCAM expressions between the different histologic stages of fatty liver steatohepatitis-cirrhosis-HCC carcinogenesis sequence were compared to healthy hepatic tissue.Overexpression of FGF19/FGFR4 significantly correlated with EpCAM as a marker of hepatic cancer stem cells within the fatty liver-steatosis-cirrhosis-HCC sequence.This is the first study to elucidate FGF19/FGFR4 signaling in favor of HCC cells developing as indicated by increased EpCAM within the carcinogenesis sequence from fatty liver to hepatocellular carcinoma. Our study has the potential to yield novel and cost effective screening strategies for HCC patients.
Abstract An unprecedented [4+2] cycloaddition of in situ generated azoalkenes with arylacetic acids has been developed under the catalysis of isothiourea. The reaction provided an efficient approach to the synthesis of 4,5‐dihydropyridazin‐3(2 H )‐one derivatives in moderate to good yields (up to 95%). magnified image
Extracting road graphs from aerial images automatically is more efficient and costs less than from field acquisition. This can be done by a post-processing step that vectorizes road segmentation predicted by CNN, but imperfect predictions will result in road graphs with low connectivity. On the other hand, iterative next move exploration could construct road graphs with better road connectivity, but often focuses on local information and does not provide precise alignment with the real road. To enhance the road connectivity while maintaining the precise alignment between the graph and real road, we propose a point-based iterative graph exploration scheme with segmentation-cues guidance and flexible steps. In our approach, we represent the location of the next move as a 'point' that unifies the representation of multiple constraints such as the direction and step size in each moving step. Information cues such as road segmentation and road junctions are jointly detected and utilized to guide the next move and achieve better alignment of roads. We demonstrate that our proposed method has a considerable improvement over state-of-the-art road graph extraction methods in terms of F-measure and road connectivity metrics on common datasets.
The programmed cell death ligand protein 1 (PD-L1) is a member of the B7 protein family and consists of 290 amino acid residues. The blockade of the PD-1/PD-L1 immune checkpoint pathway is effective in tumor treatment. Results: Two pharmacophore models were generated based on peptides and small molecules. Hypo 1A consists of one hydrogen bond donor, one hydrogen bond acceptor, two hydrophobic points and one aromatic ring point. Hypo 1B consists of one hydrogen bond donor, three hydrophobic points and one positive ionizable point. Conclusions: The pharmacophore model consisting of a hydrogen bond donor, hydrophobic points and a positive ionizable point may be helpful for designing small-molecule inhibitors targeting PD-L1.
Abstract With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.
Abstract Oxide interfaces provide a fertile ground for new forms of magnetic textures that arise from different symmetry constraints, proximity effects, and charge transfer. Here, a room‐temperature spin glass state stabilized in heterostructures of the ferromagnetic metallic oxide La 2/3 Sr 1/3 MnO 3 (LSMO) and the layered La n +1 Ni n O 3 n +1 (L‐LNO) is reported. Electron energy loss spectra and depth‐profile X‐ray photoelectron spectroscopy reveal the interfacial electron transfer from Mn 3+ to localized Ni 3+ states. Density functional theory calculations indicate that charge transfer drives the enhancement of ferromagnetic exchange interaction in the nickelate, leading to the formation of local magnetic order. The room temperature spin‐glass state in this artificially engineered LSMO/L‐LNO bilayer structure affords opportunities for designing emergent topological spin textures and spintronic devices.
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