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| 1 | Establishment and Preliminary Application of a Rapid Fluorescent Focus Inhibition Test (RFFIT) for Rabies Virus显示文摘The World Health Organization (WHO) standard assay for determining levels of the rabies virus neutralization antibody (RVNA) is the rapid fluorescent focus inhibition test (RFFIT), which is used to evaluate the immunity effect after vaccination against rabies. For RFFIT, CVS-11 was used as the challenge virus, BSR cells as the adapted cells, and WHO rabies immunoglobulin (WHO STD) as the reference serum in this study. With reference to WHO and Pasteur RFFIT procedures, a micro-RFFIT procedure adapted to our laboratory was produced, and its specificity and reproducibility were tested. We tested levels of RVNA in human serum samples after immunization with different human rabies vaccines (domestic purified Vero cell rabies vaccine (PVRV) and imported purified chick embryo cell vaccine (PCECV)) using different regimens (Zagreb regimen and Essen regimen). We analyzed the levels of RVNA, and compared the immune efficacy of domestic PVRV and imported PCECV using different immunization regimens. The results showed that the immune efficacy of domestic PVRV using the Zagreb regimen was as good as that of the imported PCECV, but virus antibodies were generated more rapidly with the Zagreb regimen than with the Essen regimen. The RFFIT procedure established in our laboratory will enhance the comprehensive detection ability of institutions involved in rabies surveillance in China. | Pengcheng Yu Xinjun Lv Xinxin Shen Qing Tang Guodong Liang | 2013 | Virologica Sinica2013，28，4： | 11 |
| 2 | Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies显示文摘Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt.Here,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.Methods:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation.Briefly,a total of 28,966 qualified images were collected.Among these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple randomiza-tion.Additionally,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist evaluation.The dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the experts.Results:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign diseases.The eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test set.In the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background,with an average DSC of 0.78±0.24 and 0.75±0.26 in the test and prospective test sets,respectively.Conclusions:The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant,and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images. | Chaofeng Li Bingzhong Jing Liangru Ke Bin Li Weixiong Xia Caisheng He Chaonan Qian Chong Zhao Haiqiang Mai Mingyuan Chen Kajia Cao Haoyuan Mo Ling Guo Qiuyan Chen Linquan Tang Wenze Qiu Yahui Yu Hu Liang Xinjun Huang Guoying Liu Wangzhong Li Lin Wang Rui Sun Xiong Zou Shanshan Guo Peiyu Huang Donghua Luo Fang Qiu Yishan Wu Yijun Hua Kuiyuan Liu Shuhui Lv Jingjing Miao Yanqun Xiang Ying Sun Xiang Guo Xing Lv | 2018 | Cancer Communications2018，38，1： | 9 |
| 3 | Generation and characterization of neutralizing human recombinant antibodies against antigenic site II of rabies virus glycoprotein显示文摘 | Lina Sun Zhe Chen Li Yu Jingshuang Wei Chuan Li Jing Jin Xinxin Shen Xinjun Lv Qing Tang Dexin Li Mifang Liang | 2012 | Applied Microbiology and Biotechnology2012，，2： | 1 |
| 4 | Channel selection against electrode shift enables robust myoelectric control without retraining显示文摘Myoelectric controlled interfaces driven by muscle activities have achieved good performance in ideal conditions and showed many potential medical-related and industrial applications.However,in practical applications,the performance could be drastically degraded due to the electrode(sensor)shift,which is inevitable in donning and doffing the system.In this study,we presented a novel channel selection method against electrode shift for robust pattern-recognition based myoelectric control.The proposed method was evaluated on twenty-four subjects,including twenty-two able-bodied subjects and two amputees,and compared with two traditional channel selection methods,i.e.,uniform selection(UNI)and sequential feature selection(SFS).We demonstrated that the offline error rates of the proposed method were significantly lower than those of the other two methods(P<0.05),and its online performance in shift conditions was comparable to that in ideal conditions.These outcomes benefit the practical applications of robust myoelectric controlled interfaces. | LV Bo HE JiaYuan SHENG XinJun DING Han ZHU XiangYang | 2021 | Science China(Technological Sciences)2021，64，8： | 0 |
| 5 | 基于内镜图像深度学习的鼻咽恶性肿瘤检测模型的建立与验证显示文摘背景与目的由于鼻咽部解剖位置隐匿且腺体增生频发,活检时恶性肿瘤的阳性率较低,从而导致初诊时鼻咽恶性肿瘤确诊延时或漏诊。本文旨在建立一种人工智能工具——基于深度学习的内镜检查,来检测鼻咽恶性肿瘤。方法建立了一种基于内镜图像的鼻咽恶性肿瘤检测模型(endoscopic imagesbased nasopharyngeal malignancies detection model,eNPM-DM),该模型由基于空间结构的全卷积网络构成,采用单独训练集和验证集对分类和分割进行微调。总共收集了28,966张合格图像。其中,自2008年1月1日至2016年12月31日,从7951例个体中获得了27,536张经活检证实的图像,按照7∶1∶2的比例随机分为训练、验证和测试集。此外,将2017年1月1日到2017年3月31日获得的1430张图像纳入预测集,用以对建立模型的性能与肿瘤专家的评价进行比较。以鼻咽镜图像为背景,对自动分割和专家手工分割进行比较,采用dice相似系数(dice similarity coefficient,DSC)评价eNPM-DM从鼻咽部内镜图像的背景中自动分割出恶性肿瘤区域的效率。结果所有图像经过病理组织学验证,包括正常对照5713(19.7%)例、鼻咽癌(nasopharyngeal carcinoma,NPC)19,107(66.0%)例、其他恶性肿瘤335(1.2%)例和3811(13.2%)例良性病变。在测试集中,eNPM-DM检测恶性肿瘤的总准确率达88.7%[95%置信区间(confidence interval,CI):87.8%–89.5%]。在预测比较阶段,eNPM-DM表现优于专家:总准确率分别为88.0%(95%CI:86.1%–89.6%)和80.5%(95%CI:77.0%–84.0%)。eNPM-DM耗时更短(40 s vs. 110.0±5.8 min),且从背景中自动分割出鼻咽恶性肿瘤区域方面表现优秀,测试集和预测集中的平均DSC分别为0.78±0.24和0.75±0.26。结论 eNPM-DM在鼻咽肿块良性/恶性诊断分类方面优于肿瘤学家评估,并且实现了从鼻咽内镜图像背景中对恶性区域自动分割。 | Chaofeng Li Bingzhong Jing Liangru Ke Bin Li Weixiong Xia Caisheng He Chaonan Qian Chong Zhao Haiqiang Mai Mingyuan Chen Kajia Cao Haoyuan Mo Ling Guo Qiuyan Chen Linquan Tang Wenze Qiu Yahui Yu Hu Liang Xinjun Huang Guoying Liu Wangzhong Li Lin Wang Rui Sun Xiong Zou Shanshan Guo Peiyu Huang Donghua Luo Fang Qiu Yishan Wu Yijun Hua Kuiyuan Liu Shuhui Lv Jingjing Miao Yanqun Xiang Ying Sun Xiang Guo Xing Lv | 2019 | 癌症2019，38，7： | 0 |
| 6 | SHUYU Robot:An Automatic Rapid Temperature Screening System显示文摘The health of people around the world and the global economy are under substantial threat from the outbreak of pandemics[1].Controlling pandemics is extremely challenging,with preventing the spread of pathogens the most important and critical step.Of all preventative actions,body temperature screening is undoubtedly highly necessary and effective[2]. | Zhao Gong Songwen Jiang Qizhi Meng Yanlei Ye Peng Li Fugui Xie Huichan Zhao Chunzhe Lv Xiaojie Wang Xinjun Liu | 2020 | Chinese Journal of Mechanical Engineering2020，33，2： | 0 |
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