CSpace  > 中国科学院计算技术研究所期刊论文  > 英文
TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2 (vol 81, pg 1004, 2020)
Niu, Gang1,2,3,4,5,6
2021-04-02
发表期刊DRUG DEVELOPMENT RESEARCH
ISSN0272-4391
页码1
摘要Faced with the current large-scale public health emergency, collecting, sorting, and analyzing biomedical information related to the "SARS-CoV-2" should be done as quickly as possible to gain a global perspective, which is a basic requirement for strengthening epidemic control capacity. However, for human researchers studying viruses and hosts, the vast amount of information available cannot be processed effectively and in a timely manner, particularly if our scientific understanding is also limited, which further lowers the information processing efficiency. We present TWIRLS (Topic-wise inference engine of massive biomedical literatures), a method that can deal with various scientific problems, such as liver cancer, acute myeloid leukemia, and so forth, which can automatically acquire, organize, and classify information. Additionally, this information can be combined with independent functional data sources to build an inference system via a machine-based approach, which can provide relevant knowledge to help human researchers quickly establish subject cognition and to make more effective decisions. Using TWIRLS, we automatically analyzed more than three million words in more than 14,000 literature articles in only 4 hr. We found that an important regulatory factor angiotensin-converting enzyme 2 (ACE2) may be involved in host pathological changes on binding to the coronavirus after infection. On triggering functional changes in ACE2/AT2R, the cytokine homeostasis regulation axis becomes imbalanced via the Renin-Angiotensin System and IP-10, leading to a cytokine storm. Through a preliminary analysis of blood indices of COVID-19 patients with a history of hypertension, we found that non-ARB (Angiotensin II receptor blockers) users had more symptoms of severe illness than ARB users. This suggests ARBs could potentially be used to treat acute lung injury caused by coronavirus infection.
关键词coronavirus cytokine storm literature mining renin angiotensin system topic inference
DOI10.1002/ddr.21817
收录类别SCI
语种英语
资助项目Public Library Association Youth Talent Project[17QNP010] ; Chongqing Health Commission COVID-19 Project[2020NCPZX01] ; Shenzhen Science and Technology Planning Project[JCYJ20170817095211560]
WOS研究方向Pharmacology & Pharmacy
WOS类目Chemistry, Medicinal ; Pharmacology & Pharmacy
WOS记录号WOS:000636141800001
出版者WILEY
引用统计
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/16820
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Niu, Gang
作者单位1.Joint Turing Darwin Lab Phil Rivers Technol Ltd, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Phil Rivers Technol Ltd, Dept Computat Biol, Beijing, Peoples R China
4.Chinese Acad Sci, West Inst Comp Technol, Chongqing, Peoples R China
5.Shenzhen Univ, Nanophoton Res Ctr, Shenzhen Key Lab Microscale Opt Informat Technol, Shenzhen, Guangdong, Peoples R China
6.Shenzhen Univ, Inst Microscale Optoelect, Shenzhen, Guangdong, Peoples R China
推荐引用方式
GB/T 7714
Niu, Gang. TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2 (vol 81, pg 1004, 2020)[J]. DRUG DEVELOPMENT RESEARCH,2021:1.
APA Niu, Gang.(2021).TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2 (vol 81, pg 1004, 2020).DRUG DEVELOPMENT RESEARCH,1.
MLA Niu, Gang."TWIRLS, a knowledge-mining technology, suggests a possible mechanism for the pathological changes in the human host after coronavirus infection via ACE2 (vol 81, pg 1004, 2020)".DRUG DEVELOPMENT RESEARCH (2021):1.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Niu, Gang]的文章
百度学术
百度学术中相似的文章
[Niu, Gang]的文章
必应学术
必应学术中相似的文章
[Niu, Gang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。