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Updated: Jul 7, 2026

High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
One-Step RAA and CRISPR-Cas13a Method for Detecting Influenza B Virus
Xinling Zhang1, Shiyu Chen1, Juezhuo Li2
1Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, People's Republic of China.
Abstract:
We developed a sensitive and specific method based on recombinase-aided amplification (RAA) and clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 13a (Cas13a). This method, named CRISPR-based Rapid and Efficient Test (CRISPRET), is designed for the early diagnosis of Influenza B (FluB) with the aim of shortening its transmission chain. We identified conserved regions in the Influenza B Virus (IBV) NS gene and designed forward and reverse primers along with crRNAs. We then established and optimised the reaction system, and Nucleic Acid Positive Reference Materials of IBV were used to evaluate the detection limit (DL) of CRISPRET. Additionally, we collected 257 clinical samples, comprising 127 samples from patients with IBV infection and 130 samples from healthy individuals, and subjected them to dual detection using CRISPRET and qPCR to evaluate the positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity of CRISPRET. We designed one forward primer, two reverse primers, and two crRNAs to establish and optimise the CRISPR ET. The method demonstrated the DL of 500 copies·μL-1 when assisted by appropriate equipment. Despite requiring auxiliary equipment and a 30-min reaction, the CRISPR ET method enables the detection of IBV nucleic acid within approximately the first 5 min, achieving high sensitivity (100%), specificity (97.69%), PPV (97.69%) and NPV (100%), with a concordance rate of 98.83% to qPCR. CRISPRET offers a simple, field-applicable, one-step method for the rapid detection of IBV. It has strong potential for field-testing applications and intelligent integration into existing diagnostic systems.
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