Evidence map›Paper›PMID 40506725›Full record

ArticleVirology journal2025

Multiplex detection of respiratory RNA viruses without amplification based on CRISPR-Cas13a immunochromatographic test strips.

Tao Wang, Wenqian Jiang, Zhiqing Huang, Zhitao Yuan, Zhiwei Chen, Jun Lin

Abstract read
In one paragraph

Article in Virology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Early Warning Signs for Monitoring Airborne Respiratory Virus Transmission.International journal of environmental research and public health · 2025
    Review
  4. Simultaneous and sensitive detection ofFrontiers in bioengineering and biotechnology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tao Wang *Institute of Applied Genomics, Fuzhou University, No.2 Xueyuan Road, Fuzhou, 350108, China.
Wenqian Jiang *Institute of Applied Genomics, Fuzhou University, No.2 Xueyuan Road, Fuzhou, 350108, China.
Zhiqing Huang *Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Zhitao YuanInstitute of Applied Genomics, Fuzhou University, No.2 Xueyuan Road, Fuzhou, 350108, China.
Zhiwei ChenFuzhou Center for Disease Control and Prevention, No. 199, Wansha Road, Zhanggang Street, Changle District, Fuzhou, 350209, China. chenzhiwei@fjmu.edu.cn.
Jun LinInstitute of Applied Genomics, Fuzhou University, No.2 Xueyuan Road, Fuzhou, 350108, China. jun@fzu.edu.cn.

Funding

Fujian Provincial Health Technology Project 2021GGA053Fujian Provincial Major Health Research Project 2022ZD01001Fujian Provincial Natural Science Foundation of China 2020J05279Fujian Provincial Science and Technology Plan Project 2022Y4003Fuzhou Health Science and Technology Plan Soft Science Research Project 2022-S-wr4Fuzhou Science and Technology Project 2021S263Fuzhou University Testing Fund of Precious Apparatus 2024T021Joint Funds for the Innovation of Science and Technology, Fujian Province 2021Y9107Joint Funds for the Innovation of Sience and Technology, Fujian Province 2020Y9140Major Research Projects for Young and Middle-aged Researchers of Fujian Provincial Health Commission 2021ZQNZD010National Key Research & Development Plan 2023YFC3304304Natural Science Foundation of Fujian Province 2022J01521Young and Middle-aged Talent Research Project of Fuzhou City 2022-S-rc5
6 · The paper itself

Abstract

Acute respiratory infections, caused by RNA viruses like respiratory syncytial virus, influenza, rhinovirus, and coronavirus, are major global health threats. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) is the gold standard for detecting these viruses but is time-consuming, complex, and requires specialized equipment. There is a need for rapid, convenient, and multi-target detection methods to improve disease prevention and control. This study developed a multi-target immunochromatographic detection method using LbuCas13a protein and "band elimination" test strips for detecting SARS-CoV-2 and influenza virus. The method's performance was evaluated by testing known 5 positive and 4 negative samples for SARS-CoV-2 and comparing results with fluorescent PCR and colloidal gold methods. Detection sensitivity was quantified using digital PCR and qPCR. The immunochromatographic test strips showed 100% concordance with fluorescent PCR and colloidal gold methods in initial clinical SARS-CoV-2 detection. Subsequently, we used dual-target immunochromatographic test strips to detect 9 SARS-CoV-2 positive samples and 9 H3N2 positive samples. However, false negatives were observed in dual-target detection of SARS-CoV-2 and H3N2 samples, likely due to low sample concentration or sample degradation. The method had a minimum detection limit of 381.75 copies/µL, as determined by digital PCR and qPCR. The developed multi-target immunochromatographic detection method offers a rapid, low-cost, and simple approach for detecting both SARS-CoV-2 and influenza viruses. With high sensitivity, specificity, and reliability, this method holds promise as a practical tool for RNA virus diagnosis and improving public health response to respiratory infections.

Indexed as

Chromatography, AffinityCOVID-19Respiratory Tract InfectionsRNA VirusesSARS-CoV-2CRISPR-Cas SystemsHumansInfluenza, HumanReagent StripsSensitivity and SpecificityReagent StripsCRISPR-Cas13aImmunochromatographic test stripMulti-target detectionRespiratory RNA virus

Identifiers

PMID40506725
PMCPMC12164057

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.