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| 1 | PhGRL2 Protein, Interacting with PhACO1, Is Involved in Flower Senescence in the Petunia显示文摘 | Yinyan Tan Juanxu Liu Fang Huang, Jiefu Guan, Shan Zhong, Na Tang, Ji Zhao, Weiyuan Yang Yixun Yu | 2014 | Molecular Plant2014，7，8： | 3 |
| 2 | Identification of prognostic biomarkers in hepatitis B virus-related hepatocellular carcinoma and stratification by integrative multi-omics analysis显示文摘 | Ruoyu Miao Haitao Luo Huandi Zhou Guangbing Li Dechao Bu Xiaobo Yang Xue Zhao Haohai Zhang Song Liu Ying Zhong Zhen Zou Yan Zhao Kuntao Yu Lian He Xinting Sang Shouxian Zhong Jiefu Huang Yan Wu Rebecca A. Miksad Simon C. Robson Chengyu Jiang Yi Zhao Haita | 2014 | Journal of Hepatology2014，，： | 2 |
| 3 | An ultradense genetic recombination map for Brassica napus, consisting of 13551 SRAP markers显示文摘 | Zudong Sun Zining Wang Jinxing Tu Jiefu Zhang Fengqun Yu Peter B. E. McVetty Genyi Li | 2007 | Theoretical and Applied Genetics2007，，8： | 1 |
| 4 | Color Doppler Velocity Profile Assessment of Portal Hemodynamics in Cirrhotic Patients with Portal Hypertension: Correlation with Esophageal Variceal Bleeding 显示文摘 | Xiao Yu Yin Mingde Liu Jiefu | 2001 | Journal of Clinical Uhrasound2001，29，1： | 1 |
| 5 | An ultradense genetic recombination map for Brassica napus, consisting of 13551 SRAP markers显示文摘 | Zudong Sun Zining Wang Jinxing Tu Jiefu Zhang Fengqun Yu Peter B. E. McVetty Genyi Li | 2007 | Theoretical and Applied Genetics2007，，8： | 1 |
| 6 | Modernization of the Organ Transplantation Program in China显示文摘 | Jiefu Huang Yilei Mao Yu Wang | 2008 | Transplantati on2008，86，： | 1 |
| 7 | Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19显示文摘With the rapid increase in the number of COVID-19 patients in Japan,the number of patients receiving oxygen at home has also increased rapidly,and some of these patients have died.An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19,and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications.Patients admitted to medical institutions in Japan from November 14,2020 to April 11,2021 and registered in the COVID-19 Registry Japan were included.Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning.Age-specific cohorts were created,and severity prediction was performed for the patient surge period.We were able to obtain a model that was able to predict severe disease with a sensitivity of 57%when the specificity was set at 90%for those aged 40e59 years,and with a specificity of 50%and 43%when the sensitivity was set at 90%for those aged 60e79 years and 80 years and older,respectively.We were able to identify lactate dehydrogenase level(LDH)as an important factor in predicting the severity of illness in all age groups.Using machine learning,we were able to identify risk factors with high accuracy,and predict the severity of the disease.We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation. | Nobuaki Matsunaga Keisuke Kamata Yusuke Asai Shinya Tsuzuki Yasuaki Sakamoto Shinpei Ijichi Takayuki Akiyama a Jiefu Yu Gen Yamada Mari Terada Setsuko Suzuki Kumiko Suzuki Sho Saito Kayoko Hayakawa Norio Ohmagari | 2022 | Infectious Disease Modelling2022，7，3： | 0 |
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