University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

The angiogenic effects of Angelica sinensis extract on HUVEC in vitro and zebrafish in vivo

Abstract: Angiogenesis plays an important role in a wide range of physiological processes such as wound healing and fetal development. Many diseases are associated with imbalances in regulation of angiogenesis, in which it is either excessive or there is insufficient blood vessel formation. Angelica sinensis (AS), commonly used in the prescriptions of Chinese medicine, is a potential candidate for curing such diseases. However, biological effects of AS on angiogenesis and underlying mechanisms are yet to be fully elucidated. This investigation describes the angiogenic effects of AS extract on human endothelial cells (HUVEC) in vitro and zebrafish in vivo. The extract was demonstrated, by XTT assay and microscopic cell counting, to stimulate the proliferation of HUVEC; in addition, flow cytometry analysis indicated that the extract increased the percentage of HUVEC in the S phase. The wound healing migration assay illustrated that a dramatic increase in migration could be measured in AS extract-treated HUVEC. Meanwhile, the number of invaded cells and the mean tube length were significantly increased in AS extract treatment groups. The extract was also demonstrated to promote changes in subintestinal vessels (SIVs) in zebrafish, one feature of angiogenesis. In addition, AS extract was found by real-time PCR to enhance vascular endothelial growth factor (VEGF) mRNA expression. In a bead-based immunoassay, higher levels of p38 and JNK 1/2 expression were also observed in effusions compared with control cells. All results suggest that Angelica sinensis extract can promote angiogenesis, and that the angiogenic effects involve p38 and JNK 1/2 phosphorylation.

The Chemical Constituents and Bioactivities of Psoralea corylifolia Linn.: A Review.

Abstract: Psoralea corylifolia Linn. (P. corylifolia) is an important medicinal plant with thousands of years of clinical application. It has been widely used in many traditional Chinese medicine formulas for the treatment of various diseases such as leucoderma and other skin diseases, cardiovascular diseases, nephritis, osteoporosis, and cancer. Phytochemical studies indicated that coumarins, flavonoids, and meroterpenes are the main components of P. corylifolia, and most of these components are present in the seeds or fruits. The extracts and active components of P. corylifolia demonstrated multiple biological activities, including estrogenic, antitumor, anti-oxidant, antimicrobial, antidepressant, anti-inflammatory, osteoblastic, and hepatoprotective activities. This paper systematically summarized literatures on the chemical constituents and biological activities of P. corylifolia, which provided useful information for the further research and development toward this potent medicinal plant.

Targeting Tumor Metabolism for Cancer Treatment: Is Pyruvate Dehydrogenase Kinases (PDKs) a Viable Anticancer Target?

Abstract: Cancer remains a lethal threat to global lives. Development of novel anticancer therapeutics is still a challenge to scientists in the field of biomedicine. In cancer cells, the metabolic features are significantly different from those of normal ones, which are hallmarks of several malignancies. Recent studies brought atypical cellular metabolism, such as aerobic glycolysis or the Warburg effect, into the scientific limelight. Targeting these altered metabolic pathways in cancer cells presents a promising therapeutic strategy. Pyruvate dehydrogenase kinases (PDKs), key enzymes in the pathway of glucose metabolism, could inactivate the pyruvate dehydrogenase complex (PDC) by phosphorylating it and preserving the substrates pyruvate, lactate and alanine for gluconeogenesis. Overexpression of PDKs could block the oxidative decarboxylation of pyruvate to satisfy high oxygen demand in cancer cells, while inhibition of PDKs could upregulate the activity of PDC and rectify the balance between the demand and supply of oxygen, which could lead to cancer cell death. Thus, inhibitors targeting PDKs represent a promising strategy for cancer treatment by acting on glycolytic tumors while showing minimal side effects on the oxidative healthy organs. This review considers the role of PDKs as regulator of PDC that catalyzes the oxidative decarboxylation of pyruvate in mitochondrion. It is concluded that PDKs are solid therapeutic targets. Inhibition of PDKs could be an attractive therapeutic approach for the development of anti-cancer drugs.

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

Abstract: In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have achieved satisfactory performance. These architectures are composed of multiple deep learning techniques in order to tackle various challenges in traffic tasks. Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature. In order to utilize such spatial information fully, it's more appropriate to formulate traffic networks as graphs mathematically. Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs). More and more works combine GNNs with other deep learning techniques to construct an architecture dealing with various challenges in a complex traffic task, where GNNs are responsible for extracting spatial correlations in traffic network. These graph-based architectures have achieved state-of-the-art performance. To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in many traffic applications. We first give guidelines to formulate a traffic problem based on graph and construct graphs from various kinds of traffic datasets. Then we decompose these graph-based architectures to discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks. What's more, we summarize some common traffic challenges and the corresponding graph-based deep learning solutions to each challenge. Finally, we provide benchmark datasets, open source codes and future research directions in this rapidly growing field.