4篇
您的检索式：作者名="Qingyi Dai"
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| 1 | Redifferentiation of human hepatoma cells induced by green tea polyphenols显示文摘 | Bo Zhou Jing Pan Fang Dai Chenyang Zhao Liping Zhang Qingyi Wei Li Yang Rongliang Zheng Zhongli Liu | 2004 | Research on Chemical Intermediates2004，，6： | 1 |
| 2 | China’s unwavering determination in protecting pregnancy and perinatal health显示文摘ACHIEVEMENTS AND CHALLENGES Before the advent ofmodern medicine,childbirth carried extremely high risk of mortality,both maternal and perinatal(the period beginning at the 20–28th week of gestation and ending at 1–4 weeks after delivery).Fortunately,with advances in prenatal healthcare,the prevalence of maternal and perinatal mortalities has significantly reduced.A press conference held on May 30,2022,by the National Health Commission of the People’s Republic of China,reviewed the major progress and achievements in Chinese maternal and child health over the past decade.It highlighted that the maternal mortality rate dropped to 161 per million in 2021,the lowest level in recorded history. | Haitong Zhe Sun Qingyi Xiang Siyuan Xu Haizhen Dai Jing Fang Hao Tang Tao Xue Haikun Wang Kun Tang Yuming Guo Xiaoxia Bai | 2022 | The Innovation2022，3，6： | 0 |
| 3 | Catechol-derived Inhibitors against Quorum Sensing from Retiboletus kauffmanii显示文摘[Objectives]This study aimed to screen anti-quorum sensing(QS)activity from the constituent of Retiboletus kauffmanii fruit body.[Methods]Chromatographic technology was used to isolate compounds,while well diffusion assay was applied to screen anti-quorum sensing activity.[Results]12 phenolic compounds were purified from extracts,three catechol-derived compounds at sub-inhibitory concentrations were found to inhibit the production of violacein in Chromobacterium violaceum for the first time.[Conclusions]Three catechol analogs isolated from R.kauffmanii fruit body extract were indicated as quorum sensing inhibitors against C.violaceum. | Liman ZHOU Lin WANG Fandong KONG Qingyun MA Qingyi XIE Haofu DAI Youxing ZHAO | 2021 | Medicinal Plant2021，12，1： | 0 |
| 4 | Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer’s disease显示文摘Alzheimer’s disease(AD)is a neurodegenerative disease that severely affects the activities of daily living in aged individuals,which typically needs to be diagnosed at an early stage.Generative adversarial networks(GANs)provide a new deep learning method that show good performance in image processing,while it remains to be verified whether a GAN brings benefit in AD diagnosis.The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods.In addition,we evaluated the research methodology and provided suggestions from the perspective of clinical application.Compared with othermethods,a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing(e.g.image denoising and segmentation).Most studies used data from public databases but lacked clinical validation,and the process of quantitative assessment and comparison in these studies lacked clinicians’participation,which may have an impact on the improvement of generation effect and generalization ability of the GAN model.The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies.Improvement methods toward better GAN architecture were also discussed in this paper.In sum,the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD,and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm. | Changxing Qu Yinxi Zou Qingyi Dai Yingqiao Ma Jinbo He Qihong Liu Weihong Kuang Zhiyun Jia Taolin Chen Qiyong Gong | 2021 | Psychoradiology2021，1，4： | 0 |
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