MSDEnet: Multi-scale detail enhanced network based on human visual system for medical image segmentation.

In medical image segmentation, accuracy is commonly high for tasks involving clear boundary partitioning features, as seen in the segmentation of X-ray images. However, for objects with less obvious boundary partitioning features, such as skin regions with similar color textures or CT images of adjacent organs with similar Hounsfield value ranges, segmentation accuracy significantly decreases. Inspired by the human visual system, we proposed the multi-scale detail enhanced network. Firstly, we designed a detail enhanced module to enhance the contrast between central and peripheral receptive field information using the superposition of two asymmetric convolutions in different directions and a standard convolution. Then, we expanded the scale of the module into a multi-scale detail enhanced module. The difference between central and peripheral information at different scales makes the network more sensitive to changes in details, resulting in more accurate segmentation. In order to reduce the impact of redundant information on segmentation results and increase the effective receptive field, we proposed the channel multi-scale module, adapted from the Res2net module. This creates independent parallel multi-scale branches within a single residual structure, increasing the utilization of redundant information and the effective receptive field at the channel level. We conducted experiments on four different datasets, and our method outperformed the common medical image segmentation algorithms currently being used. Additionally, we carried out detailed ablation experiments to confirm the effectiveness of each module.

Mesenchymal Stem Cells Accelerate Recovery of Acetic Acid-Induced Chronic Gastric Ulcer by Regulating Ekt/Akt/TRIM29 Axis.

Chronic gastric ulcer (CGU), a prevalent digestive disease, has a high incidence and is seriously harmful to human health. Mesenchymal stem cells (MSCs) have been proven to have beneficial therapeutic effects in many human diseases. Here, a CGU model induced by acetic acid in mice was used to evaluate the repair effects and potential mechanism of human umbilical cord-derived MSCs (hUC-MSCs) and hUC-MSCs derived conditioned medium (hUC-MSC-CM). We found that hUC-MSCs and hUC-MSC-CM treatment significantly repaired morphological characteristics of CGU, improved proliferation and decreased apoptosis of gastric cells, and promoted the generation of new blood vessels in granulation tissues. In addition, we could detect the homing of MSCs in gastric tissue, and MSCs may differentiate into Lgr5-positive cells. As well as this, in vitro experiments showed that hUC-MSC-CM could promote cell proliferation, stimulate cell cycle progression, and reduce the incidence of apoptosis. The transcriptome of cells and the iTRAQ proteome of gastric tissues suggest that MSCs may play a therapeutic role by increasing the expression of TRIM29. Additionally, it was found that knocking down TRIM29 significantly decreased the ameliorative effects of hUC-MSC-CM on cell apoptosis. As a result of further molecular experiments, it was found that TRIM29 is capable of phosphorylating Erk/Akt in specific cell type. As a whole, it appears that hUC-MSCs can be an effective therapeutic approach for promoting gastric ulcer healing and may exert therapeutic effects in the form of paracrine and differentiation into gastric cells.

Composite Neighbor-Aware Convolutional Metric Networks for Hyperspectral Image Classification.

Supervised classification of hyperspectral image (HSI) is generally required to obtain better performance in spectral-spatial feature learning by fully using complex pixel-and superpixel-level interdependencies with small labeled samples. Limited by the local regular convolutions, convolutional neural networks (CNNs) can only exploit information from the short-range Euclidean neighbors of a target, hindering the effectiveness of feature representation. In contrast, graph convolutional networks (GCNs) can learn long-range dependencies between non-Euclidean neighbors but usually require the input of a full graph constructed from a whole HSI, making GCNs must be trained in a full-batch manner with tremendous computational consumption. In this work, we propose a composite neighbor-aware convolutional metric network (CNCMN), aiming to learn each target's representation from its composite neighbors (i.e., both Euclidean and non-Euclidean neighbors) in a batchwise manner. Specifically, for each target in an HSI, its Euclidean neighbors are the pixels in the local square region centered on itself, and its non-Euclidean neighbors are several related nodes selected from the constructed full graph. Correspondingly, a composite convolution (CoConv) is proposed by coupling an image convolution and a graph convolution, which can perform flexible convolutions on those composite neighbors and extract adaptively fused features from them. Besides, to further boost classification, we also propose a mini-batch metric classifier to dynamically optimize interclass and intraclass distances of samples batch by batch, which is then combined with the CoConv to form the mini-batch CNCMN. Extensive experiments on three real-world HSIs demonstrate the advantages of the proposed method over mini-batch deep learning algorithms and have obtained the state-of-the-art performance in these fields. The code is available at: https://github.com/qichaoliu/HSI-CNCMN.

Semiconducting two-dimensional group VA-VA haeckelite compounds with superior carrier mobility.

A series of two-dimensional (2D) single-layer binary group VA-VA crystals, where VA represents P, As, Sb and Bi, are explored by the first-principles calculations. Unlike the orthorhombic α-phase and hexagonal ß-phase, these crystals have a tetragonal haeckelite lattice and are named as T-VA-VAs. These ultrathin 2D materials have high thermal stability and are semiconductors with moderate band gaps ranging from 0.80 to 2.68 eV (HSE06). The band gaps show a prevalent linear correlation with average ionization energies (AIEs) of different composites, and thus can be effectively designed. Furthermore, these materials exhibit superior carrier mobility, e.g. 2.96 × 103 cm-2 V-1 s-1 of T-SbBi, and considerable visible light absorption index. These novel 2D binary materials are expected to be fabricated and used as nanoelectronics and for solar energy harvesting.

In vitro photodynamic therapy of endothelial cells using hematoporphyrin monomethyl ether (Hemoporfin): Relevance to treatment of port wine stains.

Hemoporfin (hematoporphyrin monomethyl ether, HMME) is a relatively new photosensitizer that has achieved success in mediating photodynamic therapy (PDT) of port wine stains in China. However, the exact mechanism of Hemoporfin PDT on endothelial cell proliferation and apoptosis is unclear. The present study investigated the mechanism of action of HMME-PDT on endothelial cells in vitro. Human umbilical vein endothelial cells (HUVECs) were cultured in vitro. HMME-PDT treated the cells and detected the phototoxicity by cell counting kit-8 (CCK-8) assay, apoptosis by Flow cytometry assay and quantification of the secreted VEGF-A levels using ELISA and different proteins expression by quantitative real-time PCR and Western blotting. Phototoxicity was caused in an HMME and light dose-dependent manner. Apoptosis was induced as shown by Annexin-V/propidium iodide staining and morphological changes. The Bax/Bcl-2 ratio was increased as shown by Western blot for protein and RT-qPCR for mRNA. VEGF-A expression was reduced and signaling molecules in the Akt/mTOR pathway were inhibited as shown by ELISA and immunofluorescence. Hemoporfin (hematoporphyrin monomethyl ether, HMME) has achieved success in mediating photodynamic therapy (PDT) of port wine stains. The clinical success of HMME-PDT with low recurrence rates can be explained by inhibition of endothelial cell proliferation through VEGF/Akt /mTOR pathway.

CT-721, a Potent Bcr-Abl Inhibitor, Exhibits Excellent In Vitro and In Vivo Efficacy in the Treatment of Chronic Myeloid Leukemia.

Kinase inhibitors that target Bcr-Abl are highly effective in the treatment of chronic myeloid leukemia (CML). However, these inhibitors are often invalidated due to the drug resistance. Therefore, the discovery and development of novel Bcr-Abl inhibitors is required to overwhelm the drug resistance in the treatment of CML resistant to the currently used first-line Bcr-Abl inhibitors. Herein we have described a newly developed Bcr-Abl inhibitor CT-721, which displayed potent inhibitory effects on wild-type and T315I mutant Bcr-Abl. It functioned as a typically ATP-competitive inhibitor, superior to other existing Bcr-Abl inhibitors. CT-721 also demonstrated time-dependent inhibition of Bcr-Abl activation and the resultant downstream signaling transduction pathways in Bcr-Abl positive cells. Furthermore, CT-721 induced cell apoptosis and cell cycle arrest, and efficaciously inhibited tumor growth in Bcr-Abl-expressed K562 and KU812 xenograft models in a mechanism-based manner. Further PK/PD studies revealed a positive in vivo correlation between the compound concentration and inhibition of Bcr-Abl activity. Taken together, CT-721 is a potent and time-dependent Bcr-Abl kinase inhibitor, and has shown strong in vitro and in vivo anti-CML activities with a favorable pharmacokinetic profile, differentiating it from other Bcr-Abl kinase inhibitors already approved and current in development for the treatment of CML.

Development of Lingzhi or Reishi medicinal mushroom, Ganoderma lucidum (Higher Basidiomycetes) polysaccharides injection formulation.

Biochemical and pharmacological research has demonstrated that Lingzhi or Reishi medicinal mushroom Ganoderma lucidum polysaccharides (GLPS) have significant anticancer, antitumor, and antioxidant activities. To investigate the effect of injecting GLPS into hosts for clinical studies, aqueous polysaccharide extracts from G. lucidum fruit bodies were purified by deproteinization using the Sevage method, anion-exchange chromatography elution (cellulose DEAE-52 chromatography), dialysis, ethanol precipitation, and active carbon and millipore membrane filtration techniques. The purified GLPS were used for injection in mice. Polysaccharide indexes, protein, tannin, heavy metal, arsenic salt, oxalate, potassium ion, resin, pH, ignition residue measurements, evaluation criterion for allergic reactions, and total solids content of the GLPS injection were all performed using the reference methods in the Chinese Pharmacopoeia. Our results showed that polysaccharide was the key component of injection mixtures. The ignition residue and total solids content in the injection mixture were 1.4% and 2.4%, respectively. The other indices were all within the expected safety ranges. Furthermore, studies from mice functional assays showed that the injection mixture improved the antifatigue capacity of mice without any effect on weight loss/gain. In addition, the injection mixture was safe, which was confirmed by allergy testing in guinea pigs. The development of a GLPS injection offers a novel approach for future medicinal mushroom utilization and holds great commercial promise.

4-Allyl-2-meth-oxy-phenyl 3,4-dichloro-benzene-sulfonate.

The title compound, C(16)H(14)Cl(2)O(4)S, was obtained by the reaction of eugenol (4-allyl-2-meth-oxy-phenol) and 3,4-dichloro-benzene-sulfonyl chloride. The dihedral angle between the benzene rings in the mol-ecule is 40.53 (4)°. No significantly short inter-molecular contacts are observed in the crystal structure.