An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An efficient I2–DMSO reagent system-mediated multicomponent reaction strategy for the synthesis of C3-sulfenylated chromones from o-hydroxyaryl methyl ketones, rongalite, and dimethyl sulfoxide has been developed.
Abstract Metastable‐state merocyanine photoacids (MCHs) have been widely applied to various chemical, material and biomedical areas to drive or control chemical processes with light. In this work, stoichiometry and association constants have been determined for inclusion complexes of a photoacid MCH1 (( E )‐3‐(2‐(2‐hydroxystyryl)‐3,3‐dimethyl‐3 H ‐indol‐1‐ium‐1‐yl)propane‐1‐sulfonate) with β ‐cyclodextrin ( β ‐CD), 2‐hydroxypropyl‐ β ‐CD (HP‐ β ‐CD), γ ‐CD and HP‐ γ ‐CD by means of UV‐Vis absorption spectroscopic titrations. The inclusion complexes were studied to enhance acidity and chemical stability. Kinetic study showed that CDs stabilized the acidic metastable state and slowed its thermal relaxation. The acidity of the ground and metastable state (p K a GS and p K a MS ) increased upon addition of CDs. The p K a MS of [MCH1 ⋅ ( γ ‐CD) 2 ] is as low as 0.92 in comparison with 2.24 for MCH1, which is close to the lowest p K a MS values (1.20 and 1.03) reported previously, in which case the MCH1 was structurally modified with alkylammonium side chains. Addition of CDs also significantly enhanced the chemical stability of MCH1 against hydrolysis, which is one of the major concerns for the application of MCHs. In particular, the addition of HP‐ β ‐CD increased the half‐life of MCH1 in aqueous solution more than four‐fold. Moreover, the quantum chemical calculations confirmed the stoichiometry and analyzed the binding sites and hydrogen bonds of the inclusion complexes.
Transforming growth factor β 1 (TGF-β1), as the most abundant signaling molecule in bone matrix, is essential for bone homeostasis. However, the signaling transduction of TGF-β1 in the bone-forming microenvironment remains unknown. Here, we showed that microRNA-191 (miR-191) was downregulated during osteogenesis and further decreased by osteo-favoring TGF-β1 in bone marrow mesenchymal stem cells (BMSCs). MiR-191 was lower in bone tissues from children than in those from middle-aged individuals and it was negatively correlated with collagen type I alpha 1 chain (
Abstract Background Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is “large p and small n” in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification. Results This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies–Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected. Conclusions The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.
A transfection formulation is successfully developed to deliver nucleic acids by adding an auxiliary lipid (DOTAP) to the peptide, and the transfection efficiency of pDNA reaches 72.6%, which is close to Lipofectamine 2000. In addition, the designed KHL peptide–DOTAP complex exhibits good biocompatibility by cytotoxicity and hemolysis analysis. The mRNA delivery experiment indicates that the complex had a 9- or 10-fold increase compared with KHL or DOTAP alone. Intracellular localization shows that KHL/DOTAP can achieve good endolysosomal escape. Our design provides a new platform for improving the transfection efficiency of peptide vectors.
Although a large number of studies have been performed to study the dispersion behavior of spherical nanoparticles (NPs) in the polymer matrix, little effort has been directed to anisotropic NPs via simulation, which is convenient for controlling the physical parameters compared to experiment. In this work we adopt molecular dynamics simulation to study polymer nanocomposites filled with anisotropic NPs such as graphene and carbon nanotubes (CNTs). We investigate the effects of the grafting position, grafting density, the length and flexibility of the grafted chains on the dispersion of graphene and CNTs. In particular, we find that when the grafting position is located on the surface center of the graphene or the middle of the CNT, the dispersion state is the best, leading to the greatest stress-strain behavior. Meanwhile, the mechanical property can be further strengthened by introducing chemical couplings in the interfacial region, by chemically tethering the grafted chains to the matrix chains. To monitor the processing effect, we exert a dynamic periodic shear deformation in the x direction with its gradient in the y direction. Polymer chains are found to align in the x direction, graphene sheets align in the xoz plane and CNTs orientate in the z direction. We study the effects of the shear amplitude, the shear frequency, polymer-NP interaction strength and volume fraction of NPs on the stress-strain behavior. We also observe that the relaxation process following the shear deformation deteriorates the mechanical performance, resulting from the disorientation of polymer chains and NPs. In general, this work could provide valuable guidance in manipulating the distribution and alignment of graphene and CNTs in the polymer matrix.
Accurately predicting cancer driver genes remains challenging due to the increasing size and complexity of cancer genomic data. In this study, HGTDG is proposed, a heterogeneous graph transformer framework for predicting cancer driver genes and exploring downstream tasks. The framework includes a heterogeneous graph construction module that constructs a gene-protein heterogeneous network based on KEGG pathways and the protein-protein interactions from the STRING database. In addition, the framework introduces a novel heterogeneous graph transformer module that uses multi-head attention mechanisms for gene node embedding. The transformer module can capture dedicated representations for genes and edges. Finally, the generated gene embeddings are fed into the classification module to classify genes into driver and non-driver genes. The experiment results show that HGTDG outperforms the state-of-the-art methods regarding the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC).
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