8 Papers
Bin Wang is an academic researcher from Tianjin Medical University Cancer Institute and Hospital. The author has contributed to research in topics: Cancer & Internal medicine. The author has an hindex of 3, co-authored 4 publications.
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Papers
Anlotinib optimizes anti-tumor innate immunity to potentiate the therapeutic effect of PD-1 blockade in lung cancer
TL;DR: A role for anlotinib in the innate immune cells in the tumor microenvironment and a potentially synergistic anti-tumor combination with immune checkpoint inhibition are described.
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Rhamnetin induces apoptosis in human breast cancer cells via the miR-34a/Notch-1 signaling pathway.
Lan Lan,Yue Wang,Zhanyu Pan,Bin Wang,Zhensong Yue,Zhansheng Jiang,Ling Li,Cong Wang,Hongmei Tang +8 more
TL;DR: The results of the present study demonstrated that rhamnetin induced apoptosis in human breast cancer cells via the miR-34a/Notch-1 signaling pathway.
Efficacy of Traditional Chinese Medicine in Treatment and Prophylaxis of Radiation-Induced Oral Mucositis in Patients Receiving Radiotherapy: A Randomized Controlled Trial.
Cong Wang,Peiguo Wang,Huaqiang Ouyang,Jing Wang,Lining Sun,Yanwei Li,Dongying Liu,Zhansheng Jiang,Bin Wang,Zhanyu Pan +9 more
TL;DR: CHIN presented an obvious advantage in preventing radiation-induced oral mucositis compared with rhEGF spray, and body mass index in the treatment group exhibited advantage over control group after radiotherapy.
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Moxibustion for the Treatment of Cancer and its Complications: Efficacies and Mechanisms
Shanshan Lu,Bin Wang,Jiaqi Wang,Yi Guo,Shanshan Li,Suhong Zhao,Yuanzhen Yang,Yiting Feng,Zhifang Xu +8 more
TL;DR: Clinical studies demonstrating moxibustion’s ability to improve the efficacy of radiotherapy and chemotherapy and control tumor progression are examined, providing a scientific basis for the clinical application of moxIBustion in anticancer treatment and reducing the side effects of cancer therapies.
10
Diagnostic efficacy of deep learning-based prediction of origin for patients with cancers of unknown primary.
TL;DR: A diagnostic model on CUP is developed using deep learning based on genomic and clinical data and preliminarily applied in clinic, demonstrating the efficiency of this Artificial Intelligence model for the diagnosis in CUP patients.