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3 results on '"Jing, Xinhui"'

1. Successful laparoscopic removal of ovarian borderline cystadenoma in patient with multiorgan hemangioma and ventriculoperitoneal shunt: a case report and literature review.

Author

Zhang, Chenyi and Jing, Xinhui

Subjects
*CEREBROSPINAL fluid shunts, *CYSTADENOMA, *PNEUMOPERITONEUM, *LITERATURE reviews, *LAPAROSCOPIC surgery, *DIAGNOSIS, *HEMANGIOMAS
Abstract

As medical diagnosis and treatment level improved, patients with ventriculoperitoneal shunt (VPS) live longer and may develop conditions that need laparoscopic surgery. The safety of laparoscopy in patients with VPS continues to be challenged due to pneumoperitoneum. Here, we report a patient with medical history of VPS and hemangioma, diagnosed with ovarian borderline mucinous cystadenoma, received laparoscopic surgery in supine position and 10 mmHg pneumoperitoneum pressure, in which no clamping or externalizing catheter, no perioperative or postoperative complications. We also present a literature review and discuss the precautions needing considering during laparoscopy. For patients with VPS, laparoscopic surgery can be recognized as a potentially safe and feasible procedure. [ABSTRACT FROM AUTHOR]

Published
2021
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2. APGVAE: Adaptive disentangled representation learning with the graph-based structure information.

Author

Ke, Qiao, Jing, Xinhui, Woźniak, Marcin, Xu, Shuang, Liang, Yunji, and Zheng, Jiangbin

Subjects
*ARTIFICIAL neural networks, *SUPERVISED learning, *CONTENT-based image retrieval, *IMAGE retrieval
Abstract

Neural networks are used to learn task-oriented high-level representations in an end-to-end manner by building a multi-layer neural network. Generation models have developed rapidly with the emergence of deep neural networks. But it still has problems with the insufficient authenticity of generated images, the deficiency of diversity, consistency, and unexplainability in the generation process. Disentangled representation is an effective method to learn a high-level feature representation and realize the interpretability of deep neural networks. We propose a general disentangled representation learning network with variational autoencoder network as the basic framework for the image generation process. The graph-based structure of the priors is embedded in the last module of the deep encoder network to build the feature spaces by the class, task-oriented, and task-unrelated information respectively. Meanwhile the priors should be adaptively modified with the task relevance of a generated image. And the semi-supervised learning is further involved in the disentangled representation network framework to reduce the requirements of label and extend the majority of feature space under the task-unrelated feature assumption. Experimental results show that the proposed method is efficient for various types of images and has a good potential for further research and development. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Highly efficient removal of Sb(V) from water by franklinite-containing nano-FeZn composites.

Author

Wu, Huiqing, Wu, Qingping, Zhang, Jumei, Gu, Qihui, Guo, Weipeng, Rong, Shun, Zhang, Yongxiong, Wei, Xianhu, Wei, Lei, Sun, Ming, Li, Aimei, and Jing, Xinhui

Subjects
*ANTIMONY, *METAL content of water, *METAL nanoparticles, *CARCINOGENS, *GRAPHENE
Abstract

The existence of toxic and carcinogenic pentavalent antimony in water is a great safety problem. In order to remove antimony(V) from water, the purpose of this study was to prepare a novel graphene nano iron zinc (rGO/NZV-FeZn) photocatalyst via hydrothermal method followed by ultrasonication. Herein, weakly magnetic nano-Fe–Zn materials (NZV-FeZn, GACSP/NZV-FeZn, and rGO/NZV-FeZn) capable of rapid and efficient Sb(V) adsorption from water were prepared and characterised. In particular, rGO/NZV-FeZn was shown to comprise franklinite, Fe0, and graphite. Adsorption data were fitted by a quasi-second-order kinetic equation and Langmuir model, revealing that among these materials, NZV-FeZn exhibited the best Sb removal performance (543.9 mgSb gNZV-FeZn−1, R2 = 0.951). In a practical decontamination test, Sb removal efficiency of 99.38% was obtained for a reaction column filled with 3.5 g of rGO/NZV-FeZn. Column regenerability was tested at an initial concentration of 0.8111 mgSb L−1, and the treated water obtained after five consecutive runs complied with the GB5749-2006 requirement for Sb. rGO/NZV-FeZn was suggested to remove Sb(V) through adsorption-photocatalytic reduction and flocculation sedimentation mechanisms and, in view of its high cost performance, stability, and upscalable synthesis, was concluded to hold great promise for source water and wastewater treatment. [ABSTRACT FROM AUTHOR]

Published
2021
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