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Your search keyword '"Zhang Rui-zhe"' showing total 21 results
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21 results on '"Zhang Rui-zhe"'

6. Selective inhibition of melanoma and basal cell carcinoma cells by short-lived species, long-lived species, and electric fields generated from cold plasma.

Author

Chien, Po-Chien, Chen, Chao-Yu, Cheng, Yun-Chien, Sato, Takehiko, and Zhang, Rui-Zhe

Subjects
BASAL cell carcinoma, LOW temperature plasmas, ELECTRIC fields, SKIN cancer, MELANOMA
Abstract

This study investigated the effects of atmospheric-pressure cold plasma-generated short-lived species, long-lived species, and electric fields on skin melanoma and basal cell carcinoma cells (A2058 cells, BCC cells) and normal cells (BJ cells, Detroit 551 cells). Previous studies showed that either plasma-generated species or electric fields can cause cell apoptosis, but the effects of plasma-generated short-lived species, long-lived species, and electric fields on skin cancer cells have not been fully investigated yet. In this research, we adjusted the distance between the cells and plasma–medium interface to treat the cells with different concentrations of short-lived species such as •OH. A phosphate-buffered saline (PBS)–agarose gel was used to cover the PBS solution surface to prevent the plasma-generated species entering the liquid, ensuring the cells were treated with the electric field only. We compared cell viability and apoptosis to determine which factor had a greater impact on the cells. The results showed that the penetration depth of •OH in PBS reached ∼0.6 mm. With our experimental setup, long-lived species and electric field generated by the plasma did not have significant effects on either the normal or cancer skin cells. By contrast, the short-lived species significantly inhibited the viability and induced apoptosis of skin cancer cells but not the normal skin cells. By comparing the effects of plasma-generated short-lived species, long-lived species, and electric field on skin cancer cells (A2058, BCC) and normal cells (BJ, Detroit 551), this study verified that short-lived species in plasma inhibit skin cancer cells more than normal skin cells. [ABSTRACT FROM AUTHOR]

Published
2021
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10. Preparation of stable and biocompatible MOF@SiO2nanoparticles for Oxybutynin loading and pelvic floor muscle repair

Author

Li, Ai-Hong, Zhang, Jie, Feng, Li-Jun, Zhang, Rui-Zhe, Li, Zhi-Feng, and Peng, Xiao-Ying

Abstract

The pelvic floor muscle (PFM) is a critical structure maintaining the support system of pelvic organs, primarily composed of the anal support belt. Structural abnormalities and functional disorders may lead to clinical pathological changes, resulting in pelvic floor dysfunction (PFD), significantly impacting patient health. To address the challenges in PFD treatment, we first constructed a novel Zn4L2(phen)4for drug delivery and characterized its structure and fluorescence properties. Subsequently, we developed a stable and biocompatible drug, MOFs@SiO2nanoparticles, loaded with Oxybutynin. Detailed characterization of their structure was performed. An in vitro cellular model of PFD was established through mechanical injury, and our synthesized novel Zn4L2(phen)4@SiO2-Oxybutynin particles successfully alleviated the induced upregulation of MDA and downregulation of GPX4 associated with PFD, demonstrating the particle's efficacy in treating PFD by regulating ferroptosis. This study highlights the significant potential of this novel preparation strategy in the biomedical field.

Published
2024
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16. A novel perception based image categorization algorithm

Author

Zhang Rui-zhe, Tian Li-yan, Huang Jing-hua, and Yuan Jia-zheng

Subjects
Topological property, Contextual image classification, business.industry, Segmentation-based object categorization, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Naive Bayes classifier, ComputingMethodologies_PATTERNRECOGNITION, Automatic image annotation, Categorization, Computer Science::Computer Vision and Pattern Recognition, Computer vision, Artificial intelligence, business, Algorithm, Image retrieval, Feature detection (computer vision), Mathematics
Abstract

Image categorization plays an important role in the field of image retrieval and automatic annotation. Most existing algorithms adopted the low-level visual features to represent an image and the well-known Bag-of-words model followed by the SVM classifier was employed to fulfill the classification task. In this paper, instead of using the traditional low-level visual features, we employ the perception theory and propose a novel image categorization algorithm based on the representation of images' topological properties, i.e., an image can be represented by a low-dimensional features (e.g., Euler characteristic, the number of holes) which is called the topological properties. Given the new representations of images, a naive Bayes classifier is performed to fulfill the categorization task. The experimental results on the well-known image dataset show that based on the topological representation, the image categorization performance can be well improved.

Published
2009

17. A Novel Generalized SVM Algorithm with Application to Region-Based Image Retrieval

Author

Yuan Jia-zheng, Bao Hong, Zhang Rui-zhe, Wang Yu-jian, and Huang Jing-hua

Subjects
Computer science, business.industry, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Image segmentation, Content-based image retrieval, Automatic image annotation, Kernel (image processing), Image texture, Visual Word, Artificial intelligence, business, Image retrieval, Algorithm
Abstract

Support vector machines (SVM) has been widely applied in the area of content-based image retrieval in order to learn high-level concepts from low-level image features. Most existing SVM based image retrieval algorithms only rely on global-based features to represent the image content, which obviously can not well reflect the image semantic content. Region-based representations are far more close to the image content. However, such representations are of variable length and the Gaussian kernel is inappropriate in this situation. In this paper, a novel generalized SVM algorithm is proposed, which takes into account both low-level features and structural information of the image, in order to solve the problem of region-based image retrieval via SVM framework. Firstly, for a given image, salient regions are extracted and the concept of Salient Region Adjacency Graph is proposed to represent the image semantics. Secondly, based on the SRAG, a novel generalized structure kernel based SVM algorithm is constructed for content-based image retrieval. Experiments show that the proposed method shows better performance in image semantic retrieval than traditional method.

Published
2009

21. Bimetallic Cage-Based Metal-Organic Frameworks for Electrochemical Hydrogen Evolution Reaction with Enhanced Activity.

Author

Zhang RZ, Lu LL, Chen ZH, Zhang X, Wu BY, Shi W, and Cheng P

Abstract

A cage-based metal-organic framework (Ni-NKU-101) with biphenyl-3,3',5,5'-tetracarboxylic acid was synthesized via solvothermal method. Ni-NKU-101 contains two types of cages based on trinuclear and octa-nuclear nickel-clusters that are connected with each other by the 4-connected ligands, to form a 3D framework with a new topology. A mixed-metal strategy was used to synthesize isostructural bimetallic MOFs of M x Ni 1-x -NKU-101 (M=Mn, Co, Cu, Zn). The electrocatalytic studies showed that the hydrogen evolution reaction (HER) activity of Cu x Ni 1-x -NKU-101 is much higher than that of other M x Ni 1-x -NKU-101 catalysts in acidic aqueous solution, owing to the synergistic effect of the bimetallic centers. The optimized Cu 0.19 Ni 0.81 -NKU-101 has an overpotential of 324 mV at 10 mA cm -2 and a Tafel slope of 131 mV dec -1 . The mechanism of HER activity over these bimetallic MOF-based electrocatalysts are discussed in detail., (© 2022 Wiley-VCH GmbH.)

Published
2022
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