Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

Randomized, Double-Blind, Placebo-Controlled Phase III Trial of Apatinib in Patients With Chemotherapy-Refractory Advanced or Metastatic Adenocarcinoma of the Stomach or Gastroesophageal Junction

Abstract: PurposeThere is currently no standard treatment strategy for patients with advanced metastatic gastric cancer experiencing progression after two or more lines of chemotherapy. We assessed the efficacy and safety of apatinib, a novel vascular endothelial growth factor receptor 2 tyrosine kinase inhibitor, in patients with advanced gastric or gastroesophageal junction adenocarcinoma for whom at least two lines of prior chemotherapy had failed.Patients and MethodsThis was a randomized, double-blind, placebo-controlled phase III trial. Patients from 32 centers in China with advanced gastric or gastroesophageal junction adenocarcinoma, for whom two or more prior lines of chemotherapy had failed, were enrolled. Patients were randomly assigned to oral apatinib 850 mg or placebo once daily. The primary end points were overall (OS) and progression-free survival (PFS).ResultsBetween January 2011 and November 2012, 267 patients were enrolled. Median OS was significantly improved in the apatinib group compared with t...

Characterization of Surface EMG Signal Based on Fuzzy Entropy

Abstract: Fuzzy entropy (FuzzyEn), a new measure of time series regularity, was proposed and applied to the characterization of surface electromyography (EMG) signals. Similar to the two existing related measures ApEn and SampEn, FuzzyEn is the negative natural logarithm of the conditional probability that two vectors similar for m points remain similar for the next m+1 points. Importing the concept of fuzzy sets, vectors' similarity is fuzzily defined in FuzzyEn on the basis of exponential function and their shapes. Besides possessing the good properties of SampEn superior to ApEn, FuzzyEn also succeeds in giving the entropy definition in the case of small parameters. Its performance on characterizing surface EMG signals, as well as independent, identically distributed (i.i.d.) random numbers and periodical sinusoidal signals, shows that FuzzyEn can more efficiently measure the regularity of time series. The method introduced here can also be applied to other noisy physiological signals with relatively short datasets.

Caspase 3-mediated stimulation of tumor cell repopulation during cancer radiotherapy

Abstract: In cancer treatment, apoptosis is a well-recognized cell death mechanism through which cytotoxic agents kill tumor cells. Here we report that dying tumor cells use the apoptotic process to generate potent growth-stimulating signals to stimulate the repopulation of tumors undergoing radiotherapy. Furthermore, activated caspase 3, a key executioner in apoptosis, is involved in the growth stimulation. One downstream effector that caspase 3 regulates is prostaglandin E(2) (PGE(2)), which can potently stimulate growth of surviving tumor cells. Deficiency of caspase 3 either in tumor cells or in tumor stroma caused substantial tumor sensitivity to radiotherapy in xenograft or mouse tumors. In human subjects with cancer, higher amounts of activated caspase 3 in tumor tissues are correlated with markedly increased rate of recurrence and death. We propose the existence of a cell death-induced tumor repopulation pathway in which caspase 3 has a major role.

Scalable collaborative filtering using cluster-based smoothing

Abstract: Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approach has been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approach has been proposed to alleviate these problems, but this approach tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-art collaborative filtering algorithms.