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73 results on '"Cheng, Zhuo"'

13. Comprehensive analysis of mitophagy in HPV-related head and neck squamous cell carcinoma

Author

Li Yanan, Liang Hui, Cheng Zhuo, Ding Longqing, and Sun Ran

Subjects
Multidisciplinary
Abstract

Head and neck squamous cell carcinoma (HNSCC) is a common tumour type in otorhinolaryngology, and its occurrence is related to long-term exposure to tobacco and alcohol. Recently, HPV infection has become an increasingly important contributor to HNSCC, and HPV-associated HNSCC has a different clinical course and better prognosis than non-HPV-associated HNSCC. However, the exact molecular mechanism of HNSCC is unclear. Here, we obtained data from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) to analyse the mitophagy process and related influencing factors of HPV-associated HNSCC via the integration of bioinformatics analysis and experimental validation. We found that in HPV-associated HNSCC, process of mitophagy affects tumour development, immune cell infiltration and prognosis. In the mitophagy process of HPV-related HNSCC: NOS2, IL17REL, TMSB15A, TUBB4A and other hub genes showed significantly higher expression levels than in non-HPV-related HNSCC. Furthermore, this was also confirmed by quantitative real-time PCR (qRT‒PCR), which was used to detect the expression of differentially expressed genes in HNSCC tissues. Furthermore, we found that the unique immunological characteristics by expressing CD8+ T cell in a high level in HPV-related HNSCC, and the scores obtained from the score model affected the prognosis of patients. In conclusion, our study revealed the unique biomolecular signature of mitophagy in HPV-associated HNSCC, which may contribute to the development of precise treatment regimens for HPV-associated HNSCC patients.

Published
2023

16. Clinical and biomarker analyses of sintilimab plus gemcitabine and cisplatin as first-line treatment for patients with advanced biliary tract cancer

Author

Zeng, Tian-mei, primary, Yang, Guang, additional, Lou, Cheng, additional, Wei, Wei, additional, Tao, Chen-jie, additional, Chen, Xi-yun, additional, Han, Qin, additional, Cheng, Zhuo, additional, Shang, Pei-pei, additional, Dong, Yu-long, additional, Xu, He-ming, additional, Guo, Lie-ping, additional, Chen, Dong-sheng, additional, Song, Yun-jie, additional, Qi, Chuang, additional, Deng, Wang-long, additional, and Yuan, Zhen-gang, additional

Published
2023
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18. A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

Author

Jing Wang, Mindi Ma, Xuexin He, Rui Zhou, Xiaohui Zhang, Lin Chen, Han Jiang, Shuang Wu, Wenbin Qian, Zexin Chen, Liu Feng, Mei Tian, Cheng Zhuo, Miaoqi Ni, Chentao Jin, Teng Zhang, Kai Zhang, and Hong Zhang

Subjects
medicine.diagnostic_test, Receiver operating characteristic, business.industry, Proportional hazards model, Area under the curve, General Medicine, Nomogram, medicine.disease, International Prognostic Index, Positron emission tomography, medicine, Radiology, Nuclear Medicine and imaging, Fdg pet ct, Nuclear medicine, business, Diffuse large B-cell lymphoma
Abstract

Purpose This study aimed to develop a novel analytic approach based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic signature (RS) and International Prognostic Index (IPI) to predict the progression-free survival (PFS) and overall survival (OS) of patients with diffuse large B-cell lymphoma (DLBCL). Methods We retrospectively enrolled 152 DLBCL patients and divided them into a training cohort (n = 100) and a validation cohort (n = 52). A total of 1245 radiomic features were extracted from the total metabolic tumor volume (TMTV) and the metabolic bulk volume (MBV) of pre-treatment PET/CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to develop the RS. Cox regression analysis was used to construct hybrid nomograms based on different RS and clinical variables. The performances of hybrid nomograms were evaluated using the time-dependent receiver operator characteristic (ROC) curve and the Hosmer–Lemeshow test. The clinical utilities of prediction nomograms were determined via decision curve analysis. The predictive efficiency of different RS, clinical variables, and hybrid nomograms was compared. Results The RS and IPI were identified as independent predictors of PFS and OS, and were selected to construct hybrid nomograms. Both TMTV- and MBV-based hybrid nomograms had significantly higher values of area under the curve (AUC) than IPI in training and validation cohorts (all P P > 0.05). The Hosmer–Lemeshow test showed that both TMTV- and MBV-based hybrid nomograms calibrated well in the training and validation cohorts (all P > 0.05). Decision curve analysis indicated that hybrid nomograms had higher net benefits than IPI. Conclusion The hybrid nomograms combining RS with IPI could significantly improve survival prediction in DLBCL. Radiomic analysis on MBV may serve as a potential approach for prognosis assessment in DLBCL. Trial registration NCT04317313. Registered March 16, 2020. Public site: https://clinicaltrials.gov/ct2/show/NCT04317313

Published
2021

19. Attention cutting and padding learning for fine-grained image recognition

Author

Hao Luo, Hongjian Li, Duan Xiaolin, He Mingxuan, Xiangyan Zeng, and Cheng Zhuo

Subjects
Computer Networks and Communications, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Semantics, Padding, Convolutional neural network, Field (computer science), Image (mathematics), Task (computing), Hardware and Architecture, Media Technology, Computer vision, Saliency map, Artificial intelligence, business, Intelligent transportation system, Software
Abstract

Fine-grained image recognition is an important task in the field of computer vision. In fine-grained image recognition, the difference between different categories is very small. Thus, fine-grained image recognition highly depends on local features. In this paper, a novel “Attention Cutting And Padding Learning” method is proposed to learn the local features. Firstly, the image is fed to Convolutional Neural Networks, and a saliency map is gotten. According to the saliency map, the attention image is obtained. Secondly, the attention image is cut into $$N*N$$ sub-images. Every sub-image is padded by 0 and the padding size is P. All sub-images are spliced into a Cutting And Padding image. Finally, the Cutting And Padding image and the attention image are fed to CNNs to train. In this method, more local features can be learned, and the high-level semantics is not damaged. Experimental results show that the recognition accuracy of Attention Cutting And Padding Learning is 87.9%, 94.6%, and 92.4% respectively on CUB-200-2011, Stanford Cars, and FGVC-Aircraft dataset. Moreover, this method can be easily applied to biodiversity automatic monitoring, intelligent retail, intelligent transportation, and other fields to improve recognition accuracy without changing the network structure.

Published
2021

20. Targeting HECTD3-IKKα axis inhibits inflammation-related metastasis

Author

Li, Fubing, primary, Liang, Huichun, additional, You, Hua, additional, Xiao, Ji, additional, Xia, Houjun, additional, Chen, Xi, additional, Huang, Maobo, additional, Cheng, Zhuo, additional, Yang, Chuanyu, additional, Liu, Wenjing, additional, Zhang, Hailin, additional, Zeng, Li, additional, Wu, Yingying, additional, Ge, Fei, additional, Li, Zhen, additional, Zhou, Wenhui, additional, Wen, Yi, additional, Zhou, Zhongmei, additional, Liu, Rong, additional, Jiang, Dewei, additional, Xie, Ni, additional, Liang, Bin, additional, Liu, Zhenzhen, additional, Kong, Yanjie, additional, and Chen, Ceshi, additional

Published
2022
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24. A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy

Author

Mindi Ma, Qinming Zhang, Kexin Shi, Jianhua Feng, Haifeng Hou, Zexin Chen, Lin Chen, Lei Ren, Cheng Zhuo, Liu Feng, Yufei Chen, Shuang Wu, Liao Yi, Teng Zhang, Jianing Deng, Xiaofeng Dou, Wang Xiawan, Yao Ding, Hong Zhang, Le Xue, Congcong Yu, and Mei Tian

Subjects
medicine.medical_specialty, Focus (geometry), Statistical parametric mapping, Logistic regression, Pediatrics, 030218 nuclear medicine & medical imaging, Temporal lobe, 03 medical and health sciences, Epilepsy, 0302 clinical medicine, Sørensen–Dice coefficient, Fluorodeoxyglucose F18, Positron Emission Tomography Computed Tomography, medicine, Humans, Radiology, Nuclear Medicine and imaging, Child, Retrospective Studies, Glucose metabolism, medicine.diagnostic_test, business.industry, Deep learning, General Medicine, medicine.disease, Magnetic Resonance Imaging, Epilepsy, Temporal Lobe, Positron emission tomography, Positron emission tomography (PET), Positron-Emission Tomography, Original Article, Radiology, Abnormality, business, 030217 neurology & neurosurgery
Abstract

Purpose Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). Methods We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. Results The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31–0.44, P = 0.005–0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). Conclusion The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. Trial registration NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581

Published
2021

26. Hierarchical saliency mapping for weakly supervised object localization based on class activation mapping

Author

Hongjian Li, Cheng Zhuo, Duan Xiaolin, Meiqi Wang, and Xiangyan Zeng

Subjects
Computer Networks and Communications, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Pattern recognition, 02 engineering and technology, Object (computer science), Class (biology), Field (computer science), Hardware and Architecture, Salience (neuroscience), Video tracking, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Saliency map, Artificial intelligence, Pyramid (image processing), business, Software
Abstract

Weakly supervised object localization is a basic research in the field of computer vision. In this paper, a hierarchical saliency mapping network for object localization is proposed and designed to avoid missing detailed information of potential object. Based on the classical convolution network, we remove the fully connected part and add multiple information extraction branches. The network extracts information from convolution layers of different scales to generate Hierarchical Saliency Map. Hierarchical Saliency Maps that include Hierarchical-Class Activation Map and Hierarchical-Spatial Pyramid Saliency Map fuse deep-level features and low-level features to locate object. The datasets used for testing are Caltech-UCSD Birds 200, Caltech101 and ImageNet. Compared with Class Activation Map and Spatial Pyramid Saliency Map, the localization accuracy has been improved. This method can be used for fine-grained classification, object tracking and other fields.

Published
2020

30. Camera sensor-based contamination detection for water environment monitoring

Author

Xufan Zhang, Jun Chen, Cheng Zhuo, Dianhong Wang, and Yong Wang

Subjects
Water transport, Computer science, Health, Toxicology and Mutagenesis, Real-time computing, General Medicine, 010501 environmental sciences, Contamination, 01 natural sciences, Pollution, Identification (information), Water Quality, Sensor node, Environmental monitoring, Photography, Water environment, Environmental Chemistry, Water quality, Image sensor, Ecosystem, Water Pollutants, Chemical, Environmental Monitoring, 0105 earth and related environmental sciences
Abstract

Water environment monitoring is of great importance to human health, ecosystem sustainability, and water transport. Unlike traditional water quality monitoring problems, this paper focuses on visual perception of water environment. We first introduce the development of a customized aquatic sensor node equipped with an embedded camera sensor. Based on this platform, we present an efficient and holistic contamination detection approach, which can automatically adapt to the detection of floating debris in dynamic waters or the identification of salient regions in static waters. Our approach is specifically designed based on compressed sensing theory to give full consideration to the unique challenges in water environment and the resource constraints on sensor nodes. Both laboratory and field experiments demonstrate the proposed method can fast and accurately detect various types of water pollutants and is a better choice for camera sensor-based water environment monitoring compared with other methods.

Published
2018

31. Cyst(e)ine in nutrition formulation promotes colon cancer growth and chemoresistance by activating mTORC1 and scavenging ROS

Author

Wu, Jiao, primary, Yeung, Sai-Ching Jim, additional, Liu, Sicheng, additional, Qdaisat, Aiham, additional, Jiang, Dewei, additional, Liu, Wenli, additional, Cheng, Zhuo, additional, Liu, Wenjing, additional, Wang, Haixia, additional, Li, Lu, additional, Zhou, Zhongmei, additional, Liu, Rong, additional, Yang, Chuanyu, additional, Chen, Ceshi, additional, and Yang, Runxiang, additional

Published
2021
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35. m6A RNA methylation-mediated HNF3γ reduction renders hepatocellular carcinoma dedifferentiation and sorafenib resistance

Author

Zhou, Tengfei, primary, Li, Shichao, additional, Xiang, Daimin, additional, Liu, Junyu, additional, Sun, Wen, additional, Cui, Xiuliang, additional, Ning, Beifang, additional, Li, Xiao, additional, Cheng, Zhuo, additional, Jiang, Weiqi, additional, Zhang, Cheng, additional, Liang, Xijun, additional, Li, Liang, additional, Cheng, Xin, additional, Hui, Liu, additional, Wang, Hongyang, additional, and Ding, Jin, additional

Published
2020
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45. Differentiation of MISSLA and Fanconi anaemia by computer-aided image analysis and presentation of two novel MISSLA siblings

Author

Danyel, Magdalena, primary, Cheng, Zhuo, additional, Jung, Christine, additional, Boschann, Felix, additional, Pantel, Jean Tori, additional, Hajjir, Nurulhuda, additional, Flöttmann, Ricarda, additional, Schulz, Solveig, additional, Demuth, Ilja, additional, Sheridan, Eamonn, additional, Mundlos, Stefan, additional, Horn, Denise, additional, and Mensah, Martin A., additional

Published
2019
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49. Conversion of hepatoma cells to hepatocyte-like cells by defined hepatocyte nuclear factors

Author

Cheng, Zhuo, primary, He, Zhiying, additional, Cai, Yongchao, additional, Zhang, Cheng, additional, Fu, Gongbo, additional, Li, Hengyu, additional, Sun, Wen, additional, Liu, Changcheng, additional, Cui, Xiuliang, additional, Ning, Beifang, additional, Xiang, Daimin, additional, Zhou, Tengfei, additional, Li, Xiaofeng, additional, Xie, Weifen, additional, Wang, Hongyang, additional, and Ding, Jin, additional

Published
2018
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