中国台湾科学家发现一种抗新冠病毒老药,比瑞德西韦强10倍

2021-02-24 MedSci原创 MedSci原创

中国台湾阳明交通大学采用AI算出#新冠肺炎#解药!

中国台湾阳明交通大学采用AI算出#新冠肺炎#解药! 通过AI技术发现4款潜力老药,能够抑制新冠病毒活性,分别是巴色匹韦(Boceprevir)、特拉匹韦(Telaprevir)、奈非那韦(Nelfinavir)以及其中一种抗发炎旧药(JMY206)。部分成果已发表于国际顶尖期刊《美国化学学会月刊:纳米》(ACS Nano),目前细胞及动物实验显示其中一款药效比先前瑞德西韦强数十倍,为治疗新冠肺炎带来一线曙光。 JMY206-比瑞德西韦强10倍以上,后者是首个经过完全认证可用于治疗COVID-19病人的抗病毒药物,研究成果来自国立阳明交通大学生物科学与技术学院(NYCU)杨金文院长领导研究团队。JMY 206的治疗作用也已在动物实验中得到证明,它可能是对抗COVID-19的潜在口服药物。   原始出处: Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurpo

版权声明:
本网站所有内容来源注明为“梅斯医学”或“MedSci原创”的文字、图片和音视频资料,版权均属于梅斯医学所有。非经授权,任何媒体、网站或个人不得转载,授权转载时须注明来源为“梅斯医学”。其它来源的文章系转载文章,或“梅斯号”自媒体发布的文章,仅系出于传递更多信息之目的,本站仅负责审核内容合规,其内容不代表本站立场,本站不负责内容的准确性和版权。如果存在侵权、或不希望被转载的媒体或个人可与我们联系,我们将立即进行删除处理。
在此留言
评论区 (8)
#插入话题
  1. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2022-05-12 杰夫谈

    做完双盲实验再说吧

    0

  2. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
  3. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2021-03-06 474576351

    优秀

    0

  4. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2021-02-27 T2DM终结者

    这么nb 药物组学和AI一结合 简直就是旧药大洗牌

    0

  5. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2021-02-24 公卫新人

    新冠肺炎,疫情何时才能消失

    0

  6. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2021-02-24 ms5000001255976811

    优秀的人

    0

  7. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2021-02-24 神盾医疗局局长Jack

    来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。

    0

  8. [GetPortalCommentsPageByObjectIdResponse(id=1219027, encodeId=e407121902ee5, content=做完双盲实验再说吧, beContent=null, objectType=article, channel=null, level=null, likeNumber=25, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220404/da41fbf85ff14bf48dc25c8ed6f54958/c6adb3f74d514c41a344d8d28c694117.jpg, createdBy=5ca42405846, createdName=杰夫谈, createdTime=Thu May 12 12:20:56 CST 2022, time=2022-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1802672, encodeId=f3c218026e2f0, content=<a href='/topic/show?id=acfae485578' target=_blank style='color:#2F92EE;'>#科学家发现#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=31, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=74855, encryptionId=acfae485578, topicName=科学家发现)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=e572480, createdName=旅苦化文_208, createdTime=Sun Aug 29 02:32:34 CST 2021, time=2021-08-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=945921, encodeId=1cb794592156, content=优秀, beContent=null, objectType=article, channel=null, level=null, likeNumber=63, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200801/19f50cb64c18415eaa3f0911d2873c50/7ab97989a34747339c05d63b82d6663d.jpg, createdBy=efbb4899905, createdName=474576351, createdTime=Sat Mar 06 11:11:07 CST 2021, time=2021-03-06, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=943914, encodeId=5ed3943914fd, content=这么nb 药物组学和AI一结合 简直就是旧药大洗牌, beContent=null, objectType=article, channel=null, level=null, likeNumber=113, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20210226/f23705539a22465dad49b185641246f4/269e005bb68348558ceb01a4a8056073.jpg, createdBy=a54d5390453, createdName=T2DM终结者, createdTime=Sat Feb 27 11:53:05 CST 2021, time=2021-02-27, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1029160, encodeId=d2ee1029160a0, content=新冠肺炎,疫情何时才能消失, beContent=null, objectType=article, channel=null, level=null, likeNumber=35, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=f0620, createdName=公卫新人, createdTime=Wed Feb 24 20:32:34 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926914, encodeId=c0cb926914ef, content=优秀的人, beContent=null, objectType=article, channel=null, level=null, likeNumber=217, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=fc475462808, createdName=ms5000001255976811, createdTime=Wed Feb 24 17:56:52 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926911, encodeId=6950926911b5, content=来自台湾大学研究团队针对加快新冠的潜在药物的研究开发了SARS-CoV-2的药物数据库DockCoV2,以预测台湾地区食品药品监督管理局和台湾地区国民健康保险批准的3109种清单药物与新冠病毒有关的5种蛋白质(刺突蛋白、3C样蛋白酶(3CLpro)、RNA依赖性RNA聚合酶(RdRp)、木瓜蛋白酶(PLpro)、核衣壳(N )蛋白和2种宿主蛋白血管紧张素转换酶II(ACE2)和跨膜丝氨酸蛋白酶II(TMPRSS2))的结合亲和力。该研究结果发表在《核酸研究》(Nucleic Acids Research)杂志上。, beContent=null, objectType=article, channel=null, level=null, likeNumber=183, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20200529/3611e9bb629c4e5b81fbd8ce784b06cc/128487eae357460bb98f67d86230c218.jpg, createdBy=ab235268530, createdName=神盾医疗局局长Jack, createdTime=Wed Feb 24 17:39:21 CST 2021, time=2021-02-24, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=926909, encodeId=d5f892690902, content=厉害!, beContent=null, objectType=article, channel=null, level=null, likeNumber=161, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20220519/c2ab253484ee4527a2d4e9589a4821ac/45de9bf494a54becb2ea4369c9d11e85.jpg, createdBy=7a3710, createdName=健康达人, createdTime=Wed Feb 24 17:34:05 CST 2021, time=2021-02-24, status=1, ipAttribution=)]
    2021-02-24 健康达人

    厉害!

    0

相关资讯

全球新冠病毒人体挑战试验获批

多家外媒报道称,英国政府当地时间17日发表声明称,该国临床试验伦理机构已经批准了一项新冠病毒人体试验,将90名成年志愿者暴露于新冠病毒环境中,这也是全球获批的首个新冠病毒“人体挑战试验”。

Science:新冠病毒出现重要突变,会自己删除S蛋白基因以骗过疫苗

根据一项重磅新研究的结果,新冠病毒大量出现了一种特殊突变,可能会导致现在的疫苗失效。研究于2月3日发表在《科学》上,标题为“Recurrent deletions in the SARS-

Nat Commu:两项研究显示新冠病毒感染后产生抗体两个月内迅速衰减

在人们首次感染新冠病毒后,人体产生的抗体能够提供多长时间的保护作用一直广受关注。经过长时间的辩论,法国科学家日前在一项研究中找到了早期答案。

世卫:新冠病毒由实验室泄露可能性极低,水貂、猫等也可能是新冠病毒宿主

1月14日,世界卫生组织病毒溯源专家组成员来到武汉,并于1月28日结束隔离。之后,中国-世卫组织专家组共同走访了白沙洲贸易市场、华南海鲜市场、湖北省疾病预防控制中心、武汉市疾病预防控制中心、湖北省动物

群体免疫破灭:在高突变的新冠病毒下,群体免疫成为泡影

群体免疫这个概念,从疫情开始到现在一直饱受争议。中国疾病预防控制中心研究员邵一鸣表示,当接种了疫苗、能够有一定免疫屏障的人在人群中达到一定比例时,病毒或者病菌就很难再传播了。就目前情况看,人群里有80

Lancet Respir Med:聚乙二醇干扰素λ治疗可加速清除新冠病毒

干扰素λ-1是一种III型干扰素,参与先天抗病毒反应,具有抗呼吸道病原体的活性。