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| 1 | Current management of chemotherapy-induced neutropenia in adults:key points and new challenges显示文摘Chemotherapy-induced neutropenia(CIN)is a potentially fatal and common complication in myelosuppressive chemotherapy.The timing and grade of CIN may play prognostic and predictive roles in cancer therapy.CIN is associated with older age,poor functional and nutritional status,the presence of significant comorbidities,the type of cancer,previous chemotherapy cycles,the stage of the disease,specific chemotherapy regimens,and combined therapies.There are many key points and new challenges in the management of CIN in adults including:(1)Genetic risk factors to evaluate the patient’s risk for CIN remain unclear.However,these risk factors urgently need to be identified.(2)Febrile neutropenia(FN)remains one of the most common reasons for oncological emergency.No consensus nomogram for FN risk assessment has been established.(3)Different assessment tools[e.g.,Multinational Association for Supportive Care in Cancer(MASCC),the Clinical Index of Stable Febrile Neutropenia(CISNE)score model,and other tools]have been suggested to help stratify the risk of complications in patients with FN.However,current tools have limitations.The CISNE score model is useful to support decision-making,especially for patients with stable FN.(4)There are still some challenges,including the benefits of granulocyte colony stimulating factor treatment and the optimal antibiotic regimen in emergency management of FN.In view of the current reports,our group discusses the key points,new challenges,and management of CIN. | Committee of Neoplastic Supportive-Care(CONS),China Anti-Cancer Association Committee of Clinical Chemotherapy,China Anti-Cancer Association Yi Ba Yuankai Shi Wenqi Jiang Jifeng Feng Ying Cheng Li Xiao Qingyuan Zhang Wensheng Qiu Binghe Xu Ruihua Xu Bo Shen Zhiguo Luo Xiaodong Xie Jianhua Chang Mengzhao Wang Yufu Li Yuerong Shuang Zuoxing Niu Bo Liu Jun Zhang Li Zhang Herui Yao Conghua Xie Huiqiang Huang Wangjun Liao Gongyan Chen Xiaotian Zhang Hanxiang An Yanhong Deng Ping Gong Jianping Xiong Qinghua Yao Xin An Cheng Chen Yanxia Shi Jialei Wang Xiaohua Wang Zhiqiang Wang Puyuan Xing Sheng Yang Chenfei Zhou | 2020 | Cancer Biology & Medicine2020，17，4： | 2 |
| 2 | Processing Centroids of Smearing Star Image of Star Sensor显示文摘 | Yufu Liao Enhai Liu Jianyong Zhong Hui Zhang Xin Wang | 2014 | Mathematical Problems in Engineering2014，，： | 1 |
| 3 | Transparent polyimide/graphene oxide nanocomposite with improved mdisture barrier property显示文摘 | Tseng I H Liao Yufu Chiang Jenchi | 2012 | Materials Chemistry and Physics2012，136，1： | 1 |
| 4 | Density Map Guided Region Localization for End-to-End Small Object Detection显示文摘With the advancement of society and science and technology, the demand for detecting small objects in practical scenarios becomes stronger. Such objects are only represented by relatively small coverage of pixels, and the features are degraded severely after being extracted by a deep convolutional neural network, which is detrimental to the detection performance for small objects. Therefore, an intuitive solution is to increase the resolution of small objects by cropping the original image. In this paper, we propose a simple but effective object density map guided region localization module (DMGRL) to locate and crop the regions of interest where small objects may exist. Firstly, the density map of the objects is estimated by object density map estimation network, and then the coordinates of the small object regions are calculated;Secondly, the continuous differentiable affine transformation is utilized to crop these regions so that the detector with DMGRL can be trained end-to-end instead of two-stage training. Finally, the all prediction results of input image and cropped region images are merged together to output the final detection results by non maximum suppression (NMS). Extensive experiments demonstrate the superior performance of the detector incorporated DMGRL. | Bo LI Kai HUANG Junhui LI Yufu LIAO | 2023 | Journal of Systems Science and Information2023，11，6： | 0 |
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