Evidence map›Paper›PMID 37032875›Full record

ArticleFrontiers in microbiology2023

CDetection.v2: One-pot assay for the detection of SARS-CoV-2.

Xinge Wang, Yangcan Chen, Xuejia Cheng, Si-Qi Wang, Yanping Hu, Yingmei Feng, Ronghua Jin, Kangping Zhou, Ti Liu, Jianxing Wang and 8 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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

7 citing papers in PubMed, 13 citations in OpenAlex.

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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

18 authors at 11 institutions in 2 countries.

Xinge WangState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
Yangcan ChenState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
Xuejia ChengBeijing SynsorBio Technology Co., Ltd., Beijing, China.
Si-Qi WangState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
Yanping HuState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
Yingmei FengDepartment of Science and Technology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Ronghua JinBeijing Ditan Hospital, Capital Medical University, Beijing, China.
Kangping ZhouHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Ti LiuShandong Center for Disease Control and Prevention, Jinan, China.
Jianxing WangShandong Center for Disease Control and Prevention, Jinan, China.
Kai PanHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Bing LiuTonghua Central Hospital, Tonghua, Jilin, China.
Jie XiangTongji Medical College of Huazhang, Wuhan Jinyintan Hospital, University of Science and Technology, Wuhan, China.
Yanping WangHubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China.
Qi ZhouState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
Ying ZhangState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
Weiye PanBeijing SynsorBio Technology Co., Ltd., Beijing, China.
Wei LiState Key Laboratory of Stem Cell and Reproductive Biology, Chinese Academy of Sciences, Institute of Zoology, Beijing, China.
University of Chinese Academy of Sciences · CNHubei Provincial Center for Disease Control and Prevention · CNInstitute for Stem Cell Biology and Regenerative Medicine · INShandong Center for Disease Control and Prevention · CNSyngenta (China) · CNBeijing Ditan Hospital · CNCapital Medical University · CNHubei Provincial Hospital of Traditional Chinese Medicine · CNInstitute of Zoology · CNJinyintan Hospital · CNTonghua Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The ongoing 2019 coronavirus disease pandemic (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants, is a global public health threat. Early diagnosis and identification of SARS-CoV-2 and its variants plays a critical role in COVID-19 prevention and control. Currently, the most widely used technique to detect SARS-CoV-2 is quantitative reverse transcription real-time quantitative PCR (RT-qPCR), which takes nearly 1 hour and should be performed by experienced personnel to ensure the accuracy of results. Therefore, the development of a nucleic acid detection kit with higher sensitivity, faster detection and greater accuracy is important. Methods: Here, we optimized the system components and reaction conditions of our previous detection approach by using RT-RAA and Cas12b. Results: We developed a Cas12b-assisted one-pot detection platform (CDetection.v2) that allows rapid detection of SARS-CoV-2 in 30 minutes. This platform was able to detect up to 5,000 copies/ml of SARS-CoV-2 without cross-reactivity with other viruses. Moreover, the sensitivity of this CRISPR system was comparable to that of RT-qPCR when tested on 120 clinical samples. Discussion: The CDetection.v2 provides a novel one-pot detection approach based on the integration of RT-RAA and CRISPR/Cas12b for detecting SARS-CoV-2 and screening of large-scale clinical samples, offering a more efficient strategy for detecting various types of viruses.

Indexed as

clinical samplesCRISPR/Cas12bmolecular diagnosisone-pot detection platformSARS-CoV-2

Identifiers

PMID37032875
PMCPMC10076661
OpenAlexW4360619076

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.