WeChat Mini Program
Old Version Features

    • 重组酶聚合酶扩增结合Cas12a输血相关病原体核酸检测方法的可行性

    Xiaohong LI,Jia FENG, Fangxin XIE,Beirui WANG,Miao HE,Yonggang ZHANG,Feng MA,Qiang CHEN

    Chinese Journal of Blood Transfusion（2020）

    Cited 1|Views16
    Abstract
    目的 探讨重组酶聚合酶扩增(RPA)结合Cas12a检测输血相关病原体的可行性.方法 1)根据输血相关病原体HIV、HBV、HCV基因组保守区设计、筛选相应Cas12a crRNA.2)据crRNA靶序列区叠瓦状设计RPA巢式扩增内、外侧引物,凝胶电泳法和荧光探针法筛选巢式RPA内、外侧引物.3)通过质粒或病毒模拟标本梯度稀释,将含不同拷贝数或含量的靶序列RPA扩增,扩增产物以Cas12a检测评价RPA结合Cas12a的检测下限.4)用HIV、HBV、HCV相应近源病毒HTLV、WMHBV、HGBV的同源序列的重组质粒来评价所选crRNA的特异性.结果 建立起RPA结合Cas12a核酸检测系统,16份重组质粒的检测下限为10 cp,8份EcoHIV病毒模拟标本的检测下限为1fg,HIV、HBV、HCV 3种重组质粒及RPA扩增产物的交叉检测间未出现错误检出;10例HIV阳性确证标本的检测结果均为阳性.结论 RPA结合Cas12a检测HIV、HBV、HCV核酸是1种检出限和特异性均较好的核酸检测方法.
    More
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined