Institution
Sichuan University
Education•Chengdu, China•
About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.
Topics: Population, Catalysis, Cancer, Adsorption, Randomized controlled trial
Papers published on a yearly basis
Papers
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Fudan University1, Nanchang University2, Nanjing Medical University3, Harbin Medical University4, Hebei Medical University5, Peking Union Medical College6, China Medical University (PRC)7, Sun Yat-sen University8, Qingdao University9, Fujian Medical University10, Sichuan University11, Bengbu Medical College12, Fourth Military Medical University13, Tianjin Medical University Cancer Institute and Hospital14, Shanghai Jiao Tong University15, Third Military Medical University16, Zhejiang University17, Shantou University18
TL;DR: These data show that apatinib treatment significantly improved OS and PFS with an acceptable safety profile in patients with advanced gastric cancer refractory to two or more lines of prior chemotherapy.
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...
711 citations
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TL;DR: In this paper, the authors describe new developments for the controlled fabrication of monodisperse emulsions using microfluidics and use glass capillary devices to generate single, double, and higher order emulsion with exceptional precision.
707 citations
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TL;DR: In this paper, the development in the area of polyethersulfone (PES) membrane modification subjected to RO, UF, NF, gas separation (GS), and biomedical applications is discussed.
705 citations
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TL;DR: The efficient generation of 1O2 using the PS/CNTs system without any light irradiation can be employed for the selective oxidation of aqueous organic compounds under neutral conditions with the mineralization and toxicity evaluated.
703 citations
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TL;DR: Artificial intelligence algorithms integrating chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19 with similar accuracy as compared to a senior radiologist.
Abstract: For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
701 citations
Authors
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Name | H-index | Papers | Citations |
---|---|---|---|
Jie Zhang | 178 | 4857 | 221720 |
Robin M. Murray | 171 | 1539 | 116362 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Xiaoyuan Chen | 149 | 994 | 89870 |
Yi Yang | 143 | 2456 | 92268 |
Xinliang Feng | 134 | 721 | 73033 |
Chuan He | 130 | 584 | 66438 |
Lei Zhang | 130 | 2312 | 86950 |
Jian Zhou | 128 | 3007 | 91402 |
Shaobin Wang | 126 | 872 | 52463 |
Yi Xie | 126 | 745 | 62970 |
Pak C. Sham | 124 | 866 | 100601 |
Wei Chen | 122 | 1946 | 89460 |
Bo Wang | 119 | 2905 | 84863 |