Diagnostic efficiency of RPA/RAA integrated CRISPR-Cas technique for COVID-19: A systematic review and meta-analysis.
Diagnostic efficiency of RPA/RAA integrated CRISPR-Cas technique for COVID-19: A systematic review and meta-analysis.
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RPA/RAA 集成 CRISPR-Cas 技术对 COVID-19 的诊断效率:系统评价和荟萃分析
DOI:
10.1371/journal.pone.0276728
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发表时间:
2022
期刊:
影响因子:
3.7
通讯作者:
Li, Heng
中科院分区:
文献类型:
--
作者:
Zhang, Xiaoyu;Ge, Xiao;Shen, Fangyuan;Qiao, Jinjuan;Zhang, Yubo;Li, Heng
To evaluate the diagnostic value of recombinase polymerase/ aided amplification (RPA/RAA) integrated clustered regularly interspaced short palindromic repeats (CRISPR) in the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We searched relevant literature on CRISPR technology for COVID-19 diagnosis using "novel coronavirus", "clustered regularly interspaced short palindromic repeats" and "RPA/RAA" as subject terms in PubMed, Cochrane, Web of Science, and Embase databases. Further, we performed a meta-analysis after screening the literature, quality assessment, and data extraction. The pooled sensitivity, specificity and a rea under the summary receiver operator characteristic curve (AUC) were 0.98 [95% confidence interval (CI):0.97–0.99], 0.99 (95% CI: 0.97–1.00) and 1.00 (95% CI: 0.98–1.00), respectively. For CRISPR-associated (Cas) proteins-12, the sensitivity, specificity was 0.98 (95% CI: 0.96–1.00), 1.00 (95% CI: 0.99–1.00), respectively. For Cas13, the sensitivity and specificity were 0.99 (95% CI: 0.97–1.00) and 0.95 (95% CI: 0.91–1.00). The positive likelihood ratio (PLR) was 183.2 (95% CI: 28.8, 1166.8); the negative likelihood ratio (NLR) was 0.02 (95% CI: 0.01, 0.03). RPA/RAA integrated with CRISPR technology is used to diagnose coronavirus disease-19 (COVID-19) with high accuracy and can be used for large-scale population screening.
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DOI:
10.1002/advs.202001300
发表时间:
2020-10
期刊:
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
影响因子:
--
作者:
Ma P;Meng Q;Sun B;Zhao B;Dang L;Zhong M;Liu S;Xu H;Mei H;Liu J;Chi T;Yang G;Liu M;Huang X;Wang X
通讯作者:
Wang X
影响因子:
14.8
作者:
Liu, Tina Y.;Knott, Gavin J.;Smock, Dylan C. J.;Desmarais, John J.;Son, Sungmin;Bhuiya, Abdul;Jakhanwal, Shrutee;Prywes, Noam;Agrawal, Shreeya;Derby, Maria Diaz de Leon;Switz, Neil A.;Armstrong, Maxim;Harris, Andrew R.;Charles, Emeric J.;Thornton, Brittney W.;Fozouni, Parinaz;Shu, Jeffrey;Stephens, Stephanie, I;Kumar, G. Renuka;Zhao, Chunyu;Mok, Amanda;Iavarone, Anthony T.;Escajeda, Arturo M.;McIntosh, Roger;Kim, Shin E.;Dugan, Eli J.;Pollard, Katherine S.;Tan, Ming X.;Ott, Melanie;Fletcher, Daniel A.;Lareau, Liana F.;Hsu, Patrick D.;Savage, David F.;Doudna, Jennifer A.
通讯作者:
Doudna, Jennifer A.
影响因子:
16.6
作者:
Ding X;Yin K;Li Z;Lalla RV;Ballesteros E;Sfeir MM;Liu C
通讯作者:
Liu C
影响因子:
12.6
作者:
Lee CY;Degani I;Cheong J;Lee JH;Choi HJ;Cheon J;Lee H
通讯作者:
Lee H
影响因子:
6.1
作者:
Li S;Huang J;Ren L;Jiang W;Wang M;Zhuang L;Zheng Q;Yang R;Zeng Y;Luu LDW;Wang Y;Tai J
通讯作者:
Tai J