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388 results on '"Sun, Yingli"'

17. Safety evaluation of single-sperm cryopreservation technique applied in intracytoplasmic sperm injection

Author

Zhang, Duanjun, primary, Yao, Wenliang, additional, Zhang, Mingliang, additional, Yang, Lijuan, additional, Li, Lin, additional, Liu, Shujuan, additional, Jiang, Xianglong, additional, Sun, Yingli, additional, Hu, Shuonan, additional, Huang, Yufang, additional, Xue, Jie, additional, Zheng, Xiaoting, additional, Xiong, Qi, additional, Chen, Shenghui, additional, and Zhu, Haiqin, additional

Published
2024
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33. Supplementary Data from High-Throughput Screening Identifies Two Classes of Antibiotics as Radioprotectors: Tetracyclines and Fluoroquinolones

Author

Kim, Kwanghee, primary, Pollard, Julianne M., primary, Norris, Andrew J., primary, McDonald, J. Tyson, primary, Sun, Yingli, primary, Micewicz, Ewa, primary, Pettijohn, Kelly, primary, Damoiseaux, Robert, primary, Iwamoto, Keisuke S., primary, Sayre, James W., primary, Price, Brendan D., primary, Gatti, Richard A., primary, and McBride, William H., primary

Published
2023
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36. GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status

Author

Zhao, Wei, Chen, Weidao, Li, Ge, Lei, Du, Yang, Jiancheng, Chen, Yanjing, Jiang, Yingjia, Wu, Jiangfen, Ni, Bingbing, Sun, Yeqi, Wang, Shaokang, Sun, Yingli, Li, Ming, and Liu, Jun

Abstract

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoid false negatives. Deep learning methods based on computed tomography (CT) images may improve the noninvasive prediction of EGFR mutation status and potentially help clinicians guide biopsies by visual methods. Inspired by the potential inherent links between EGFR mutation status and invasiveness information, we hypothesized that the predictive performance of a deep learning network can be improved through extra utilization of the invasiveness information. Here, we created a novel explainable transformer network for EGFR classification named gated multiple instance learning transformer (GMILT) by integrating multi-instance learning and discriminative weakly supervised feature learning. Pathological invasiveness information was first introduced into the multitask model as embeddings. GMILT was trained and validated on a total of 512 patients with adenocarcinoma and tested on three datasets (the internal test dataset, the external test dataset, and The Cancer Imaging Archive (TCIA) public dataset). The performance (area under the curve (AUC) $=0.772$ on the internal test dataset) of GMILT exceeded that of previously published methods and radiomics-based methods (i.e., random forest and support vector machine) and attained a preferable generalization ability (AUC $=0.856$ in the TCIA test dataset and AUC $=0.756$ in the external dataset). A diameter-based subgroup analysis further verified the efficiency of our model (most of the AUCs exceeded 0.772) to noninvasively predict EGFR mutation status from computed tomography (CT) images. In addition, because our method also identified the “core area” of the most suspicious area related to the EGFR mutation status, it has the potential ability to guide biopsies.

Published
2024
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48. Additional file 1 of Epigenetic reprogramming of Runx3 reinforces CD8 + T-cell function and improves the clinical response to immunotherapy

Author

Liu, Zongzhi, Li, Xiang, Gao, Yibo, Liu, Jiejie, Feng, Yating, Liu, Yang, Wang, Junyun, Wang, Chunmeng, Wang, Dongrui, He, Jie, Han, Weidong, Mei, Qian, and Sun, Yingli

Abstract

Additional file 1: Figure S1. Large scale demethylation is initiated by DAC. Figure S2. Dynamic expression changes of important immune related genes. Figure S3. Tsne analysis of T cells in peripheral blood and spleen after Runx3 conditional knockout in CD8+T cell. Figure S4. Tsne analysis of T cells in peripheral blood and spleen after PD-1 and DAC+PD-1 treatment in Runx3flfl mice. Figure S5. CD8+ level did not changed when treated with PD-1 or DAC/PD-1. Table 1. Patient charcateristics. Table 2. Primer sequences for mRNA quantification. Table 3. Antibodies for flow cytometry. Table 4. Antibodies for Mass cytometry.

Published
2023
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