ABSTRACT
Pulmonary fibrosis (PF) is a chronic and progressive disease characterized by fibrosis and interstitial pneumonia. It has similar clinical symptoms to "Fei Bi" and "Fei Wei" as described in the traditional Chinese medicine (TCM) classic Jingui Yaolue written by Zhang Zhongjing in the Han Dynasty. This study explored the potential of Maimendong Decoction (MMDD). MMDD consists of Ophiopogon japonicus (L.f) (ophiopogonis), Pinellia ternata (Thunb.) Breit. (pinellia), Panax ginseng C. A. Mey. (ginseng), Glycyrrhiza uralensis Fisch. (glycyrrhiza), Zizi phus jujuba Mill. (jujuba), and Oryza sativa L. (oryza sativa), with the function of nourishing the lung and stomach, and reducing the effect of reverse qi. It has been used clinically for over two thousand years to treat conditions like "Fei Bi" and "Fei Wei". Previous research suggests that MMDD and its individual herbal extracts have anti-fibrotic effects. The main focus of MMDD in treating PF is to reduce inflammatory cytokines, inhibit pro-fibrotic factors and oxidative stress, promote differentiation and homing of bone marrow mesenchymal stem cells, and enhance cell autophagy activity. This review summarized the clinical applications, mechanisms, and pharmacological effects of MMDD in treating PF based on existing clinical applications and experimental research. It also discussed current issues and prospects, aiming to provide a reference for further research on the mechanism of PF, drug development, and clinical trials.
ABSTRACT
Unsupervised graph-level representation learning has recently shown great potential in a variety of domains, ranging from bioinformatics to social networks. Plenty of graph contrastive learning methods have been proposed to generate discriminative graph-level representations recently. They typically design multiple types of graph augmentations and enforce a graph to have consistent representations under different views. However, these techniques mostly neglect the intrinsic hierarchical structure of the graph, resulting in a limited exploration of semantic information for graph representation. Moreover, they often rely on a large number of negative samples to prevent collapsing into trivial solutions, while a great need for negative samples may lead to memory issues during optimization in graph domains. To address the two issues, this paper develops an unsupervised graph-level representation learning framework named Hierarchical Graph Contrastive Learning (HGCL), which investigates the hierarchical structural semantics of a graph at both node and graph levels. Specifically, our HGCL consists of three parts, i.e., node-level contrastive learning, graph-level contrastive learning, and mutual contrastive learning to capture graph semantics hierarchically. Furthermore, the Siamese network and momentum update are further involved to release the demand for excessive negative samples. Finally, the experimental results on both benchmark datasets for graph classification and large-scale OGB datasets for transfer learning demonstrate that our proposed HGCL significantly outperforms a broad range of state-of-the-art baselines.
Subject(s)
Deep Learning , Learning , Benchmarking , Computational Biology , MotionABSTRACT
We report on the detection of electric field-induced second harmonic generation (EFISHG) from the anode interfaces of reduced and oxidized Fe-doped SrTiO3 (Fe:STO) single crystals. For the reduced crystal, we observe steady enhancements of the susceptibility components as the imposed dc-voltage increases. The enhancements are attributed to a field-stabilized electrostriction, leading to Fe:Ti-O bond stretching and bending in Fe:Ti-O6 octahedra. For the oxidized crystal, no obvious structural changes are observed below 16 kV/cm. Above 16 kV/cm, a sharp enhancement of the susceptibilities occurs due to local electrostrictive deformations in response to oxygen vacancy migrations away from the anode. Differences between the reduced and oxidized crystals are explained by their relative oxygen vacancy and free carrier concentrations which alter internal electric fields present at the Pt/Fe:STO interfaces. Our results show that the optical SHG technique is a powerful tool for detecting structural changes near perovskite-based oxide interfaces due to field-driven oxygen vacancy migration.
ABSTRACT
Chemical constituents from the acetone extract of twigs of Manglietia hookeri were isolated and purified by various column chromatographic methods over silica gel and sephadex LH-20, and preparative TLC. The structures of these compounds were identified on the basis of physicochemical properties and spectral analysis, including NMR and MS spectra. Six eudesmane sesquiterpenes were obtained and their structures were identified as trans-eudesmane-4, 11-diol(1), ß-eudesmol(2), (-) -10-epi-5ß-hydroxy-ß-eudesmol (3), epi-carrisone (4), 6-hydroxy-eudesm-4(14) -ene(5) and gynurenol(6). All the compounds were isolated from this plant for the first time. Furthermore, the 13C-NMR data of compound 3 were reported for the first time.