免费不卡中文字幕在线|久久做人人做人人综合|初尝黑人嗷嗷叫中文字幕|国产成人v片视频在线观看|欧美日本国产VA高清视频|亚洲国产精品国自产拍AV|国产欧美精品一区二区色综合|微拍国产私拍福利88精品视频

  • <button id="0gwi0"></button>
    <tfoot id="0gwi0"></tfoot>
  • <dl id="0gwi0"><acronym id="0gwi0"></acronym></dl>
    <li id="0gwi0"></li>
    <rt id="0gwi0"><acronym id="0gwi0"></acronym></rt>
  • <rt id="0gwi0"></rt>
  • New technology can help predict dangerous heart attacks

    Source: Xinhua| 2018-08-29 01:02:07|Editor: yan
    Video PlayerClose

    LONDON, Aug. 28 (Xinhua) -- Researchers have developed a new technology that can flag patients at risk of deadly heart attacks years before they occur, according to a study released on Tuesday by the University of Oxford.

    Researchers at the University of Oxford, working with colleagues in Germany and the United States, have demonstrated a technology that is capable of detecting the inflamed plaques that are prone to cause heart attacks by analyzing computed tomography (CT) images of the fat surrounding the arteries.

    It has been shown that the most dangerous plaques release chemical messengers which modify the surrounding fat. Heart attacks are usually caused by inflamed plaques in the coronary artery, causing an abrupt blockage of blood getting to the heart.

    The Fat Attenuation Index (FAI) technology was found to predict fatal heart attacks many years before they happen, with a significantly superior predictive accuracy compared with other methods. People with abnormal FAI had up to nine times higher risk of having a fatal heart attack in the next five years. The relevant results of the technology has been published in the journal The Lancet.

    "Knowing who is at increased risk for a heart attack could allow us to intervene early enough to prevent it. I expect these biomarkers to become an essential part of standard CT coronary angiography reporting in the coming years," said Professor Charalambos Antoniades, who led the study at the University of Oxford's Division of Cardiovascular Medicine.

    TOP STORIES
    EDITOR’S CHOICE
    MOST VIEWED
    EXPLORE XINHUANET
    010020070750000000000000011105521374260311