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| 1 | Late Quaternary (30.7–9.0 cal ka BP) vegetation history in Central Asia inferred from pollen records of Lake Balikun, northwest China显示文摘 | Cheng-Bang An Shi-Chen Tao Jiaju Zhao Fa-Hu Chen Yanbin Lv Weimiao Dong Hu Li Yongtao Zhao Ming Jin Zongli Wang | 2013 | Journal of Paleolimnology2013，，2： | 1 |
| 2 | Loss of bifurcation patency after cross-over stenting of ostial lesions in superficial femoral artery: possible causes, prevention and reintervention显示文摘 | Jiang Junhao Chen Bin Dong Zhihui Shi Yun Li Weimiao Yue Jianing | 2014 | Chinese Medical Journal2014，，18： | 1 |
| 3 | Interaction between HLA-G and NK cell receptor KIR2DL4 orchestrates HER2-positive breast cancer resistance to trastuzumab显示文摘Despite the successful use of the humanized monoclonal antibody trastuzumab(Herceptin)in the clinical treatment of human epidermal growth factor receptor 2(HER2)-overexpressing breast cancer,the frequently occurring drug resistance remains to be overcome.The regulatory mechanisms of trastuzumab-elicited immune response in the tumor microenvironment remain largely uncharacterized.Here,we found that the nonclassical histocompatibility antigen HLA-G desensitizes breast cancer cells to trastuzumab by binding to the natural killer(NK)cell receptor KIR2DL4.Unless engaged by HLA-G,KIR2DL4 promotes antibody-dependent cell-mediated cytotoxicity and forms a regulatory circuit with the interferon-γ(IFN-γ)production pathway,in which IFN-γ upregulates KIR2DL4 via JAK2/STAT1 signaling,and then KIR2DL4 synergizes with the Fey receptor to increase IFN-γ secretion by NK cells.Trastuzumab treatment of neoplastic and NK cells leads to aberrant cytokine production characterized by excessive tumor growth factor-β(TGF-β)and IFN-γ,which subsequently reinforce HLA-G/KIR2DL4 signaling.In addition,TGF-β and IFN-γ impair the cytotoxicity of NK cells by upregulating PD-L1 on tumor cells and PD-1 on NK cells.Blockade of HLA-G/KIR2DL4 signaling improved the vulnerability of HER2-positive breast cancer to trastuzumab treatment in vivo.These findings provide novel insights into the mechanisms underlying trastuzumab resistance and demonstrate the applicability of combined HLA-G and PD-L1/PD-1 targeting in the treatment of trastuzumab-resistant breast cancer. | Guoxu Zheng Zhangyan Guo Weimiao Li Wenjin Xi Baile Zuo Rui Zhang Weihong Wen An-Gang Yang Lintao Jia | 2021 | Signal Transduction and Targeted Therapy2021，6，7： | 1 |
| 4 | Dust variation recorded by lacustrine sediments from arid Central Asia since ~<ce:hsp sp='0.10'/>15 cal ka BP and its implication for atmospheric circulation显示文摘 | Cheng-Bang An Jiaju Zhao Shichen Tao Yanbin Lv Weimiao Dong Hu Li Ming Jin Zongli Wang | 2010 | Quaternary Research2010，，3： | 1 |
| 5 | Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features显示文摘Recurrence is the key factor affecting the prognosis of osteosarcoma.Currently,there is a lack of clinically useful tools to predict osteosarcoma recurrence.The application of pathological images for artificial intelligence‐assisted accurate prediction of tumour out-comes is increasing.Thus,the present study constructed a quantitative histological image classifier with tumour nuclear features to predict osteosarcoma outcomes using haema-toxylin and eosin(H&E)‐stained whole‐slide images(WSIs)from 150 osteosarcoma patients.We first segmented eight distinct tissues in osteosarcoma H&E‐stained WSIs,with an average accuracy of 90.63%on the testing set.The tumour areas were auto-matically and accurately acquired,facilitating the tumour cell nuclear feature extraction process.Based on six selected tumour nuclear features,we developed an osteosarcoma histological image classifier(OSHIC)to predict the recurrence and survival of osteo-sarcoma following standard treatment.The quantitative OSHIC derived from tumour nuclear features independently predicted the recurrence and survival of osteosarcoma patients,thereby contributing to precision oncology.Moreover,we developed a fully automated workflow to extract quantitative image features,evaluate the diagnostic values of feature sets and build classifiers to predict osteosarcoma outcomes.Thus,the present study provides a novel tool for predicting osteosarcoma outcomes,which has a broad application prospect in clinical practice. | Zhan Wang Haoda Lu Yan Wu Shihong Ren Diarra mohamed Diaty Yanbiao Fu Yi Zou Lingling Zhang Zenan Wang Fangqian Wang Shu Li Xinmi Huo Weimiao Yu Jun Xu Zhaoming Ye | 2023 | CAAI Transactions on Intelligence Technology2023，8，3： | 0 |
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