|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Grape Seed Extract acting on astrocytes reveals neuronal protection against oxidative stress via interleukin-6-mediated mechanisms 显示文摘 | KAYOKO F A TETSURO O KEISUKE S | 2009 | Cell Mol Neurobiol2009，21，5： | 1 |
| 2 | Diphenhydramine transport by pH-dependent tertiary amine transport system in Caco-2 cells显示文摘 | Hiroshi M Toshiya K Kayoko A Yukiya H Ken-Ichi I | 2000 | Am J Physiol Gastrointest Liver Physiol2000，278，： | 1 |
| 3 | Elder abuse and neglect social problem revealed from 15 autopsy cases 显示文摘 | Yasuo B Masatake T | 2003 | Legal medicine2003，5，： | 1 |
| 4 | Triterpenoid saponins of aquifoliaceous plants 显示文摘 | Kayoko A Kazuko Y Shigenobu A | 1993 | Chem Pharm Bull1993，41，1： | 1 |
| 5 | Expression of Caveolin -1 in the rat temporomandibular joint显示文摘 | Kayoko NI Suzuki A Amizuka N | 2006 | Wiley Liss2006，288，1： | 1 |
| 6 | Pronounced inhibition by a natural anthocyanin,purple corn color, of 2-amino- 1 -methyl-6-phenylimidazo pyridine (PhlP)-associated colorectal carcinogenesis in male F344 rats pretreated with 1,2-dimetbylhydrazine显示文摘 | Akihiro Hagiwara Kayoko Miyashita Takumi Nakanishi et a/ | 2001 | Cancer letters2001，171，1： | 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 |
您所在的机构暂未开通此部分的功能，请联系管理员