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Institution

Sun Yat-sen University

EducationGuangzhou, Guangdong, China
About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.
Topics: Population, Cancer, Metastasis, Cell growth, Apoptosis


Papers
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Journal ArticleDOI
TL;DR: A 87-amino-acid peptide encoded by the circular form of the long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) is identified that can reduce glioblastoma proliferation via interaction with PAF1 which sequentially inhibits the transcriptional elongation of some oncogenes.
Abstract: Circular RNAs (circRNAs) are a large class of transcripts in the mammalian genome. Although the translation of circRNAs was reported, additional coding circRNAs and the functions of their translated products remain elusive. Here, we demonstrate that an endogenous circRNA generated from a long noncoding RNA encodes regulatory peptides. Through ribosome nascent-chain complex-bound RNA sequencing (RNC-seq), we discover several peptides potentially encoded by circRNAs. We identify an 87-amino-acid peptide encoded by the circular form of the long intergenic non-protein-coding RNA p53-induced transcript (LINC-PINT) that suppresses glioblastoma cell proliferation in vitro and in vivo. This peptide directly interacts with polymerase associated factor complex (PAF1c) and inhibits the transcriptional elongation of multiple oncogenes. The expression of this peptide and its corresponding circRNA are decreased in glioblastoma compared with the levels in normal tissues. Our results establish the existence of peptides encoded by circRNAs and demonstrate their potential functions in glioblastoma tumorigenesis.

446 citations

Proceedings Article
27 Sep 2018
TL;DR: SNAS as mentioned in this paper reformulates the architecture search problem as an optimization problem on parameters of a joint distribution for the search space in a cell and proposes a search gradient to leverage the gradient information in generic differentiable loss for architecture search.
Abstract: We propose Stochastic Neural Architecture Search (SNAS), an economical end-to-end solution to Neural Architecture Search (NAS) that trains neural operation parameters and architecture distribution parameters in same round of back-propagation, while maintaining the completeness and differentiability of the NAS pipeline. In this work, NAS is reformulated as an optimization problem on parameters of a joint distribution for the search space in a cell. To leverage the gradient information in generic differentiable loss for architecture search, a novel search gradient is proposed. We prove that this search gradient optimizes the same objective as reinforcement-learning-based NAS, but assigns credits to structural decisions more efficiently. This credit assignment is further augmented with locally decomposable reward to enforce a resource-efficient constraint. In experiments on CIFAR-10, SNAS takes less epochs to find a cell architecture with state-of-the-art accuracy than non-differentiable evolution-based and reinforcement-learning-based NAS, which is also transferable to ImageNet. It is also shown that child networks of SNAS can maintain the validation accuracy in searching, with which attention-based NAS requires parameter retraining to compete, exhibiting potentials to stride towards efficient NAS on big datasets. We have released our implementation at this https URL.

445 citations

Journal ArticleDOI
TL;DR: This guideline uses tables and is complemented by explanatory and descriptive notes covering the diagnosis, comprehensive treatment, and follow-up visits for gastric cancer in China.
Abstract: China is one of the countries with the highest incidence of gastric cancer. There are differences in epidemiological characteristics, clinicopathological features, tumor biological characteristics, treatment patterns, and drug selection between gastric cancer patients from the Eastern and Western countries. Non-Chinese guidelines cannot specifically reflect the diagnosis and treatment characteristics for the Chinese gastric cancer patients. The Chinese Society of Clinical Oncology (CSCO) arranged for a panel of senior experts specializing in all sub-specialties of gastric cancer to compile, discuss, and revise the guidelines on the diagnosis and treatment of gastric cancer based on the findings of evidence-based medicine in China and abroad. By referring to the opinions of industry experts, taking into account of regional differences, giving full consideration to the accessibility of diagnosis and treatment resources, these experts have conducted experts’ consensus judgement on relevant evidence and made various grades of recommendations for the clinical diagnosis and treatment of gastric cancer to reflect the value of cancer treatment and meeting health economic indexes. This guideline uses tables and is complemented by explanatory and descriptive notes covering the diagnosis, comprehensive treatment, and follow-up visits for gastric cancer.

445 citations

Journal ArticleDOI
TL;DR: It appears that the chemical properties of a drug critical to CYP3A4 inactivation include formation of reactive metabolites by CYP isoenzymes, preponderance of CYP inducers and P-glycoprotein (P-gp) substrate, and occurrence of clinically significant pharmacokinetic interactions with coadministered drugs.
Abstract: Consistent with its highest abundance in humans, cytochrome P450 (CYP) 3A is responsible for the metabolism of about 60% of currently known drugs. However, this unusual low substrate specificity also makes CYP3A4 susceptible to reversible or irreversible inhibition by a variety of drugs. Mechanism-based inhibition of CYP3A4 is characterised by nicotinamide adenine dinucleotide phosphate hydrogen (NADPH)-, time- and concentration-dependent enzyme inactivation, occurring when some drugs are converted by CYP isoenzymes to reactive metabolites capable of irreversibly binding covalently to CYP3A4. Approaches using in vitro, in silico and in vivo models can be used to study CYP3A4 inactivation by drugs. Human liver microsomes are always used to estimate inactivation kinetic parameters including the concentration required for half-maximal inactivation (K I) and the maximal rate of inactivation at saturation (k inact). Clinically important mechanism-based CYP3A4 inhibitors include antibacterials (e.g. clarithromycin, erythromycin and isoniazid), anticancer agents (e.g. tamoxifen and irinotecan), anti-HIV agents (e.g. ritonavir and delavirdine), anti-hypertensives (e.g. dihydralazine, verapamil and diltiazem), sex steroids and their receptor modulators (e.g. gestodene and raloxifene), and several herbal constituents (e.g. bergamottin and glabridin). Drugs inactivating CYP3A4 often possess several common moieties such as a tertiary amine function, furan ring, and acetylene function. It appears that the chemical properties of a drug critical to CYP3A4 inactivation include formation of reactive metabolites by CYP isoenzymes, preponderance of CYP inducers and P-glycoprotein (P-gp) substrate, and occurrence of clinically significant pharmacokinetic interactions with coadministered drugs. Compared with reversible inhibition of CYP3A4, mechanism-based inhibition of CYP3A4 more frequently cause pharmacokinetic-pharmacodynamic drug-drug interactions, as the inactivated CYP3A4 has to be replaced by newly synthesised CYP3A4 protein. The resultant drug interactions may lead to adverse drug effects, including some fatal events. For example, when aforementioned CYP3A4 inhibitors are coadministered with terfenadine, cisapride or astemizole (all CYP3A4 substrates), torsades de pointes (a life-threatening ventricular arrhythmia associated with QT prolongation) may occur. However, predicting drug-drug interactions involving CYP3A4 inactivation is difficult, since the clinical outcomes depend on a number of factors that are associated with drugs and patients. The apparent pharmacokinetic effect of a mechanism-based inhibitor of CYP3A4 would be a function of its K I, k inact and partition ratio and the zero-order synthesis rate of new or replacement enzyme. The inactivators for CYP3A4 can be inducers and P-gp substrates/inhibitors, confounding in vitro-in vivo extrapolation. The clinical significance of CYP3A inhibition for drug safety and efficacy warrants closer understanding of the mechanisms for each inhibitor. Furthermore, such inactivation may be exploited for therapeutic gain in certain circumstances.

444 citations

Journal ArticleDOI
TL;DR: By virtue of the kinetically controlled flexibility and hydrophobic pore surface, MAF-2 can adsorb large amounts of small organic molecules but excludes H2O, and can separate benzene and cyclohexane efficiently.
Abstract: The porous metal azolate framework [Cu(etz)]∞ (MAF-2, Hetz = 3,5-diethyl-1,2,4-triazole) processes an NbO type cuprous triazolate scaffold and a CsCl type hydrophobic channel system, in which the large cavities are interconnected by small apertures with pendant ethyl groups. Since the ethyl-blocked apertures behave as thermoactivated IRIS stops for the guest molecules, the gas sorption behavior of MAF-2 can be controlled by temperature, in which N2 adsorption was observed at 195 K rather than 77 K. Single-crystal X-ray structural analysis revealed that the [Cu(etz)]∞ host framework is not altered upon N2 inclusion, confirming the occurrence of the so-called “kinetically controlled flexibility”. By virtue of the kinetically controlled flexibility and hydrophobic pore surface, MAF-2 can adsorb large amounts of small organic molecules but excludes H2O. As demonstrated by single-crystal X-ray structural analyses, MAF-2 shrinks, expands, or distorts its framework to accommodate the hydrogen-bonded hexamers of ...

444 citations


Authors

Showing all 115971 results

NameH-indexPapersCitations
Yi Chen2174342293080
Jing Wang1844046202769
Yang Gao1682047146301
Yang Yang1642704144071
Peter Carmeliet164844122918
Frank J. Gonzalez160114496971
Xiang Zhang1541733117576
Rui Zhang1512625107917
Seeram Ramakrishna147155299284
Joseph J.Y. Sung142124092035
Joseph Lau140104899305
Bin Liu138218187085
Georgios B. Giannakis137132173517
Kwok-Yung Yuen1371173100119
Shu Li136100178390
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023349
20221,547
202115,594
202013,929
201911,766