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383 results on '"Zhang, Zimu"'

1. Enhancer looping protein LDB1 modulates MYB expression in T-ALL cell lines in vitro by cooperating with master transcription factors

Author

Li, Yan, Zhang, Zimu, Yu, Juanjuan, Yin, Hongli, Chu, Xinran, Cao, Haibo, Tao, Yanfang, Zhang, Yongping, Li, Zhiheng, Wu, Shuiyan, Hu, Yizhou, Zhu, Frank, Gao, Jizhao, Wang, Xiaodong, Zhou, Bi, Jiao, Wanyan, Wu, Yumeng, Yang, Yang, Chen, Yanling, Zhuo, Ran, Yang, Ying, Zhang, Fenli, Shi, Lei, Hu, Yixin, Pan, Jian, and Hu, Shaoyan

Published
2024
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3. Targeting RBM39 through indisulam induced mis-splicing of mRNA to exert anti-cancer effects in T-cell acute lymphoblastic leukemia

Author

Ji, Tongting, Yang, Yang, Yu, Juanjuan, Yin, Hongli, Chu, Xinran, Yang, Pengju, Xu, Ling, Wang, Xiaodong, Hu, Shaoyan, Li, Yizhen, Wu, Xiaochen, Liu, Wengyuan, Zhou, Bi, Wang, Wenjuan, Zhang, Shuqi, Cheng, Wei, Chen, Yanling, Shi, Lei, Li, Zhiheng, Zhuo, Ran, Zhang, Yongping, Tao, Yanfang, Wu, Di, Li, Xiaolu, Zhang, Zimu, Fan, Jun-jie, Pan, Jian, and Lu, Jun

Published
2024
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19. CDK5RAP3 is a novel super-enhancer-driven gene activated by master TFs and regulates ER-Phagy in neuroblastoma

Author

Zhuo, Ran, Zhang, Zimu, Chen, Yanling, Li, Gen, Du, Shibei, Guo, Xinyi, Yang, Randong, Tao, Yanfang, Li, Xiaolu, Fang, Fang, Xie, Yi, Wu, Di, Yang, Yang, Yang, Chun, Yin, Hongli, Qian, Guanghui, Wang, Hairong, Yu, Juanjuan, Jia, Siqi, Zhu, Frank, Feng, Chenxi, Wang, Jianwei, Xu, Yunyun, Li, Zhiheng, Shi, Lei, Wang, Xiaodong, Pan, Jian, and Wang, Jian

Published
2024
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25. Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System.

Author

Xu, Xiayue, Yun, Boxiang, Zhao, Yumin, Jin, Ling, Zong, Yanning, Yu, Guanzhen, Zhao, Chuanliang, Fan, Kai, Zhang, Xiaolin, Tan, Shiwang, Zhang, Zimu, Wang, Yan, Li, Qingli, and Yu, Shaoqing

Subjects
COMPUTER-aided diagnosis, ENDOSCOPIC surgery, NASAL surgery, COMPUTER-assisted surgery, ARTIFICIAL intelligence, DEEP learning
Abstract

Objective: We aim to construct an artificial intelligence (AI)-assisted nasal endoscopy diagnostic system capable of preliminary differentiation and identification of nasal neoplasia properties, as well as intraoperative tracking, providing an important basis for nasal endoscopic surgery. Methods: We retrospectively analyzed 1050 video data of nasal endoscopic surgeries involving four types of nasal neoplasms. Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm. Results: The Att-Res2-UNet network demonstrated the highest accuracy and precision, with the most accurate recognition results. The overall accuracy of the model established by us achieved an overall accuracy similar to that of residents (0.9707 ± 0.00984), while slightly lower than that of rhinologists (0.9790 ± 0.00348). SiamMask's segmentation range was consistent with rhinologists, with a 99% compliance rate and a neoplasm probability value ≥ 0.5. Conclusions: This study successfully established an AI-assisted nasal endoscopic diagnostic system that can preliminarily identify nasal neoplasms from endoscopic images and automatically track them in real time during surgery, enhancing the efficiency of endoscopic diagnosis and surgery. [ABSTRACT FROM AUTHOR]

Published
2025
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34. Separation and purification of antioxidant peptides from Idesia polycarpa Maxim. cake meal and study of conformational relationship between them.

Author

Dou, Lei, Zhang, Zimu, Yang, Wenqing, Chen, Yaobing, Luo, Kai, and Kan, Jianquan

Subjects
*MATRIX metalloproteinases, *DAMAGE models, *QUANTUM chemistry, *BINDING energy, *MOLECULAR pharmacology
Abstract

In this study, peptides were isolated and purified from Idesia polycarpa Maxim. cake meal for the first time, with the aim of discovering peptides with excellent antioxidant properties. Peptides were isolated and purified from the cake meal using ultrafiltration and dextran gel chromatography. Fractions with significant antioxidant activity were identified by mass spectrometry (MS) and the peptides were screened and characterized using techniques, such as network pharmacology and molecular docking. The results showed that the CIPs‐I‐F2 fractions possessed excellent antioxidant activities, and a total of seven peptides were screened, with the main targets of action including serine/threonine‐protein kinase AKT (AKT1), signal transducer and activator of transcription 3 (STAT3), and matrix metalloproteinase 9 (MMP9), among which ISKPTWADF had the highest total binding energy to the target. ISKPTWADF was synthesized in vitro by solid‐phase synthesis and showed a dose‐dependent protective effect against the hydrogen peroxide (H2O2)‐induced oxidative damage model in human hepatocellular carcinoma HepG2 cells, with its main active site on the tryptophan indole ring at position 52N‐127H. [ABSTRACT FROM AUTHOR]

Published
2024
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35. AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images.

Author

Li, Yuwen, Zheng, Jiashuo, Li, Shaokun, Wang, Chunxi, Zhang, Zimu, and Zhang, Xiujian

Subjects
OBJECT recognition (Computer vision), COST functions, FEATURE extraction, COMPUTER vision, INFRARED imaging
Abstract

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection. [ABSTRACT FROM AUTHOR]

Published
2024
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45. The BET inhibitor GNE-987 effectively induces anti-cancer effects in T-cell acute lymphoblastic leukemia by targeting enhancer regulated genes

Author

Yu, Juanjuan, primary, Yang, Yang, additional, Zhou, Rongfang, additional, Tao, Yan-Fang, additional, Zhu, Frank, additional, Jiao, Wanyan, additional, Zhang, Zimu, additional, Ji, Tongtign, additional, Li, Tiandan, additional, Fang, Fang, additional, Xie, Yi, additional, Wu, Di, additional, Zhuo, Ran, additional, Li, Xiaolu, additional, Chen, Yanling, additional, Yin, Hongli, additional, Wang, Jianwei, additional, and Pan, Jian, additional

Published
2024
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50. METTL14 promotes neuroblastoma by inhibiting YWHAH via an m6A-YTHDF1-dependent mechanism

Author

Pan, Jian, primary, Wang, Jianwei, additional, Yin, Hongli, additional, Li, Gen, additional, Wu, Di, additional, Xu, Yunyun, additional, Chen, Yanling, additional, Zhang, Ting, additional, Fei, Danhong, additional, Yang, Pengcheng, additional, Fang, Fang, additional, Tao, Yan-Fang, additional, Li, Xiao-Lu, additional, Yu, Juan-Juan, additional, Yang, Yang, additional, Li, Zhi-Heng, additional, and Zhang, Zimu, additional

Published
2023
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