Sun Yat-sen University

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.

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

Abstract: Background A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. Conclusions The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

Room-Temperature Self-Healable and Remoldable Cross-linked Polymer Based on the Dynamic Exchange of Disulfide Bonds

Abstract: Tri-n-butylphosphine (TBP) has been shown to effectively catalyze an air-insensitive disulfide metathesis reaction under alkaline conditions at room temperature. A cross-linked polysulfide containing the phosphine exhibited repeated autonomous self-healing resulting in restoration of tensile strength as a result of the dynamic exchange of disulfide bonds. Interestingly, the cross-linked polysulfide can also be reshaped and reprocessed at room temperature via the TBP-mediated reshuffling of the macromolecular networks. The mechanical properties and self-healing ability of polymeric specimens made from chopped samples remain surprisingly constant. In sharp contrast, control specimens without the phosphine catalyst or S–S bonds are neither self-healable nor reprocessable.

Reduced Graphene Oxide-Hierarchical ZnO Hollow Sphere Composites with Enhanced Photocurrent and Photocatalytic Activity

Abstract: The reduced graphene oxide (RGO)-hierarchical ZnO hollow sphere composites are prepared through a simple ultrasonic treatment of the solution containing graphene oxide (GO), Zn(CH3COO)2, DMSO, and H2O. The GO is reduced to RGO effectively, and the ZnO hollow spheres consisting of nanoparticles are uniformly dispersed on the surface of RGO sheets during the ultrasonic process. The optimum synergetic effect of RGO-ZnO composites is found at a RGO mass ratio of 3.56%, and the photocurrent and photodegradation efficiency on methylene blue of RGO-ZnO composites are improved by five times and 67%, respectively, compared with those of pure ZnO hollow spheres. The enhancements of photocurrent and photocatalytic activity can be attributed to the suppression of charge carriers recombination resulting from the interaction between ZnO and RGO.