Fang Wang

Bio: Fang Wang is an academic researcher from New York University. The author has contributed to research in topics: Medicine & Materials science. The author has an hindex of 53, co-authored 449 publications receiving 12298 citations. Previous affiliations of Fang Wang include Lanzhou University of Technology & McMaster University.

Programmed cell death pathways in cancer: a review of apoptosis, autophagy and programmed necrosis

Abstract: Programmed cell death (PCD), referring to apoptosis, autophagy and programmed necrosis, is proposed to be death of a cell in any pathological format, when mediated by an intracellular program. These three forms of PCD may jointly decide the fate of cells of malignant neoplasms; apoptosis and programmed necrosis invariably contribute to cell death, whereas autophagy can play either pro-survival or pro-death roles. Recent bulk of accumulating evidence has contributed to a wealth of knowledge facilitating better understanding of cancer initiation and progression with the three distinctive types of cell death. To be able to decipher PCD signalling pathways may aid development of new targeted anti-cancer therapeutic strategies. Thus in this review, we present a brief outline of apoptosis, autophagy and programmed necrosis pathways and apoptosis-related microRNA regulation, in cancer. Taken together, understanding PCD and the complex interplay between apoptosis, autophagy and programmed necrosis may ultimately allow scientists and clinicians to harness the three types of PCD for discovery of further novel drug targets, in the future cancer treatment.

Apatinib (YN968D1) Reverses Multidrug Resistance by Inhibiting the Efflux Function of Multiple ATP-Binding Cassette Transporters

Abstract: Apatinib, a small-molecule multitargeted tyrosine kinase inhibitor, is in phase III clinical trial for the treatment of patients with non–small-cell lung cancer and gastric cancer in China. In this study, we determined the effect of apatinib on the interaction of specific antineoplastic compounds with P-glycoprotein (ABCB1), multidrug resistance protein 1 (MRP1, ABCC1), and breast cancer resistance protein (BCRP, ABCG2). Our results showed that apatinib significantly enhanced the cytotoxicity of ABCB1 or ABCG2 substrate drugs in KBv200, MCF-7/adr, and HEK293/ABCB1 cells overexpressing ABCB1 and in S1-M1-80, MCF-7/FLV1000, and HEK293/ABCG2-R2 cells overexpressing ABCG2 (wild-type). In contrast, apatinib did not alter the cytotoxicity of specific substrates in the parental cells and cells overexpressing ABCC1. Apatinib significantly increased the intracellular accumulation of rhodamine 123 and doxorubicin in the multidrug resistance (MDR) cells. Furthermore, apatinib significantly inhibited the photoaffinity labeling of both ABCB1 and ABCG2 with [125I]iodoarylazidoprazosin in a concentration-dependent manner. The ATPase activity of both ABCB1 and ABCG2 was significantly increased by apatinib. However, apatinib, at a concentration that produced a reversal of MDR, did not significantly alter the ABCB1 or ABCG2 protein or mRNA expression levels or the phosphorylation of AKT and extracellular signal–regulated kinase 1/2 (ERK1/2). Importantly, apatinib significantly enhanced the effect of paclitaxel against the ABCB1-resistant KBv200 cancer cell xenografts in nude mice. In conclusion, apatinib reverses ABCB1- and ABCG2-mediated MDR by inhibiting their transport function, but not by blocking the AKT or ERK1/2 pathway or downregulating ABCB1 or ABCG2 expression. Apatinib may be useful in circumventing MDR to other conventional antineoplastic drugs. Cancer Res; 70(20); 7981–91. ©2010 AACR.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).