麻豆中文字幕丨欧美一级免费在线观看丨国产成人无码av在线播放无广告丨国产第一毛片丨国产视频观看丨七妺福利精品导航大全丨国产亚洲精品自在久久vr丨国产成人在线看丨国产超碰人人模人人爽人人喊丨欧美色图激情小说丨欧美中文字幕在线播放丨老少交欧美另类丨色香蕉在线丨美女大黄网站丨蜜臀av性久久久久蜜臀aⅴ麻豆丨欧美亚洲国产精品久久蜜芽直播丨久久99日韩国产精品久久99丨亚洲黄色免费看丨极品少妇xxx丨国产美女极度色诱视频www

Chinese researchers identify biomarker for early detection of Alzheimer's disease

Source: Xinhua| 2020-04-10 20:00:38|Editor: huaxia

A doctor arranges test kits at a clinic of Hunan People's Hospital in Changsha, central China's Hunan Province, Feb. 7, 2020.

Researchers from the Institute of Automation of the Chinese Academy of Sciences and other collaborators found that hippocampal radiomic features can be a promising personalized biomarker for Alzheimer's disease.

BEIJING, April 10 (Xinhua) -- Chinese researchers have identified a neuroimaging biomarker that can facilitate the early detection of Alzheimer's disease (AD).

AD is a chronic neurodegenerative disease characterized by progressive dementia. Neuroimaging techniques such as magnetic resonance imaging (MRI) can help diagnose AD. Accurate data-driven methods that can classify and characterize the neural features of AD would be powerful clinical tools.

Researchers from the Institute of Automation of the Chinese Academy of Sciences and other collaborators found that hippocampal radiomic features can be a promising personalized biomarker for AD.

In their search for suitable biomarkers, researchers proposed a novel hippocampal radiomic biomarker derived from structural MRI and systematically validated its reliability using neuroimaging data from over 1,900 individuals, including more than 700 located at six sites in China and around 1,200 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

The study showed that hippocampal radiomic features are related to the clinical features and changes in cognition ability. Long-term follow-up data in some hospitals demonstrated that the markers could be used to track the progression of the disease in high-risk subjects.

The research was published in the journal Science Bulletin.

KEY WORDS:
EXPLORE XINHUANET
010020070750000000000000011102121389649311