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2,345 results on '"Zhang, Xueli"'

18. miR-let-7a inhibits sympathetic nerve remodeling after myocardial infarction by downregulating the expression of nerve growth factor

Author

Jing Yanyan, Qi Lei, Zhang Xueli, Zheng Lu, Yang Peijin, Yin Jie, Shi Yugen, and Yan Suhua

Subjects
mir-let-7a, nerve growth factor, macrophages, sympathetic neural remodeling, myocardial infarction, Medicine
Abstract

Sympathetic hyperinnervation following myocardial infarction (MI) is one of the primary causes of ventricular arrhythmias (VAs) after MI. Nerve growth factor (NGF) is a key molecule that induces sympathetic nerve remodeling. Previous studies have confirmed that microRNA (miR)-let-7a interacts with NGF. However, whether miR-let-7a is involved in sympathetic remodeling after MI remains unknown. We aimed to investigate whether miR-let-7a was associated with the occurrence of VA after MI.

Published
2024
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29. A Computer-Aided Diagnosis System for Breast Pathology: A Deep Learning Approach with Model Interpretability from Pathological Perspective

Author

Hsu, Wei-Wen, Wu, Yongfang, Hao, Chang, Hou, Yu-Ling, Gao, Xiang, Shao, Yun, Zhang, Xueli, He, Tao, and Tai, Yanhong

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Objective: We develop a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using pathological knowledge. Methods: In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification into three classes directly, we designed a hierarchical framework with the multi-view scheme that performs lesion detection for region proposal at higher magnification first and then conducts lesion classification at lower magnification for each detected lesion. Results: The slide-level accuracy rate for three-category classification reaches 90.8% (99/109) through 5-fold cross-validation and achieves 94.8% (73/77) on the testing set. The experimental results show that the morphological characteristics and co-occurrence properties learned by the deep learning models for lesion classification are accordant with the clinical rules in diagnosis. Conclusion: The pathological interpretability of the deep features not only enhances the reliability of the proposed CAD system to gain acceptance from medical specialists, but also facilitates the development of deep learning frameworks for various tasks in pathology. Significance: This paper presents a CAD system for pathological image analysis, which fills the clinical requirements and can be accepted by medical specialists with providing its interpretability from the pathological perspective.

Published
2021

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