| 1 | 96-Week Treatment of Tenofovir Amibufenamide and Tenofovir Disoproxil Fumarate in Chronic Hepatitis B Patients显示文摘Background and Aims:Tenofovir amibufenamide(TMF)is a novel phosphoramidated prodrug of tenofovir with nonin-ferior efficacy and better bone and renal safety to tenofovir disoproxil fumarate(TDF)in 48 weeks of treatment.Here,we update 96-week comparison results.Methods:Patients with chronic hepatitis B were assigned(2:1)to receive either 25 mg TMF or 300 mg TDF with matching placebo for 96 weeks.The virological suppression was defined as HBV DNA levels<20 IU/mL at week 96.Safety was evaluated thoroughly with focusing on bone,renal,and metabolic pa-rameters.Results:Virological suppression rates at week 96 were similar between TMF and TDF group in both HBeAg-positive and HBeAg-negative populations.Noninferior efficacy was maintained in the pooled population,while it was first achieved in patients with HBV DNA≥7 or 8 log10 IU/mL at baseline.Non-indexed estimated glomerular filtration rate for renal safety assessment was adopted,while a smaller decline of which was seen in the TMF group than in the TDF group(p=0.01).For bone mineral density,patients receiv-ing TMF displayed significantly lower reduction levels in the densities of spine,hip,and femur neck at week 96 than those receiving TDF.In addition,the lipid parameters were stable after week 48 in all groups while weight change still showed the opposite trend.Conclusions:TMF maintained similar efficacy at week 96 compared with TDF with continued superior bone and renal safety profiles(NCT03903796). | Zhihong Liu Qinglong Jin Yuexin Zhang Guozhong Gong Guicheng Wu Lvfeng Yao Xiaofeng Wen Zhiliang Gao Yan Huang Daokun Yang Enqiang Chen Qing Mao Shide Lin Jia Shang Huanyu Gong Lihua Zhong Huafa Yin Fengmei Wang Peng Hu Qiong Wu Chao Pan Wen Jia Chuan Li Chang’an Sun Junqi Niu Jinlin Hou TMF Study Group | 2023 | Journal of Clinical and Translational Hepatology2023，11，3： | 3 |
| 2 | Image recognition and empirical application of desert plant species based on convolutional neural network显示文摘In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources. | LI Jicai SUN Shiding JIANG Haoran TIAN Yingjie XU Xiaoliang | 2022 | Journal of Arid Land2022，14，12： | 2 |
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