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Your search keyword '"Cheng, Xudong"' showing total 817 results
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817 results on '"Cheng, Xudong"'

18. Current Status of Research on Wet Hydrogen Sulfide/Seawater Corrosion Resistant Anti-Corrosion Coatings for Steel Structures in Marine Environments

Author

CHEN Xiangxiang, ZHANG Pu, ZENG Xian, SHEN Huanrong, CHENG Xudong, GE Shenyu, CAO Qian, ZHANG Yun

Subjects
seawater；wet hydrogen sulfide；steel structure；anti-corrosion coating；biological friendliness；present situation, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology
Abstract

Due to the complexity of the deep-sea marine environment，offshore engineering equipment such as deep-sea oil and gas exploitation has suffered from seawater erosion and chemical corrosion for a long time.In this work，the characteristics and mechanism of wet hydrogen sulfide and seawater corrosion of steel structure materials were generally described.For hydrogen sulfide-resistant and seawater corrosion-resistant coatings，the current researches of the coating preparation technology and coating performance were summarized and summarized.Furthermore，the toxicity effects of common metals and the evaluation methods of marine biological friendliness were summarized，which could provide technical support for the design of green coating formulation components and application risk assessment.

Published
2023
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19. Trimethylamine oxide and serum amyloid A in diagnosis of chronic cerebral hypoperfusion

Author

ZHANG Xin, ZHANG Jing, CHENG Xudong, ZHANG Qi, ZHOU Sen, JIA Lijun, WANG Lirong, and YU Nengwei

Subjects
chronic cerebral hypoperfusion, trimethylamine n-oxide, serum amyloid a, arterial spin labeling technique, Medicine
Abstract

ObjectiveTo investigate the diagnostic value of trimethylamine N-Oxide （TMAO） and serum amyloid A （SAA） levels in peripheral blood of patients with chronic cerebral hypoperfusion （CCH）. MethodsThe patients attending the Department of Neurology of Sichuan Provincial Peoples Hospital from February 2022 to June 2022 were divided into CCH group （n=55） and control group （n=42） based on the results of magnetic resonance arterial spin labeling （ASL）. The levels of TMAO and SAA were detected by ELISA and their diagnostic value for CCH were anaylzed by ROC curve. ResultsThe levels of TMAO （47.24±8.84 vs 40.81±8.33, t=3.639, P＜0.05） and SAA （14.04±2.70 vs 12.20±2.30, t=3.542, P＜0.05） in CCH group were significantly higher than those in control group. High levels of TMAO and SAA and history of primary hypertension were independent risk factors for the occurrence of CCH （P＜0.05）. The areas under ROC curve （AUC） of TMAO and SAA for diagnosing CCH were 0.701 （95%CI: 0.594-0.809） respectively and 0.675 （95%CI: 0.565-0.786）. The combined detection of TMAO and SAA achieved the highest AUC of 0.765 （95%CI: 0.667-0.863）. ConclusionBoth TMAO and SAA are related to the occurrence of CCH and can be used as auxiliary diagnostic indicators for CCH.

Published
2023
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50. Deep learning‐based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

Author

Yang, Meiyi, Yang, Lili, Zhang, Qi, Xu, Lifeng, Yang, Bo, Li, Yingjie, Cheng, Xudong, Zhang, Feng, Liu, Ming, and Yu, Nengwei

Subjects
MACHINE learning, MAGNETIC resonance imaging, IMAGE analysis, TRANSFORMER models, DIAGNOSIS methods, DEEP learning
Abstract

Background: Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. Purpose: To enhance the diagnosis of CCH and non‐CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision‐making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. Methods: In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1‐weighted (T1WI) and T2‐weighted (T2WI) sequence images for each patient. We propose CCH‐Network (CCHNet), an end‐to‐end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state‐of‐the‐art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. Results: CCHNet achieved an AUC of 91.6% (95% CI: 86.8–99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6–95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6–95.6) and a specificity (SP) of 90.0% (95% CI: 82.3–97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3–93.0), an ACC of 84.2% (95% CI: 70.2–93.6), a SE of 83.3% (95% CI: 68.3–95.5), and a SP of 84.7% (95% CI: 70.3–96.8). Conclusions: The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH. [ABSTRACT FROM AUTHOR]

Published
2024
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