Evidence map›Paper›PMID 35218386›Full record

ArticleApplied microbiology and biotechnology2022

Comparison of analytical sensitivity and efficiency for SARS-CoV-2 primer sets by TaqMan-based and SYBR Green-based RT-qPCR.

Yile Tao, Yang Yue, Guangyu Qiu, Zheng Ji, Martin Spillman, Zhibo Gai, Qingfa Chen, Michel Bielecki, Michael Huber, Alexandra Trkola and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Applied microbiology and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

13 citing papers in PubMed, 26 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

13 authors at 5 institutions in 2 countries.

Yile TaoInstitute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland.
Yang YueInstitute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland.
Guangyu QiuInstitute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland.
Zheng JiSchool of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China.
Martin SpillmanInstitute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland.
Zhibo GaiDepartment of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland.
Qingfa ChenInstitute for Tissue Engineering and Regenerative Medicine, Liaocheng University, Liaocheng, 252000, China.
Michel BieleckiEpidemiology, Biostatistics and Prevention Institute, University of Zurich, 8091, Zurich, Switzerland.
Michael HuberInstitute of Medical Virology, University of Zurich, 8057, Zurich, Switzerland.
Alexandra TrkolaInstitute of Medical Virology, University of Zurich, 8057, Zurich, Switzerland.
Qiyuan WangKey Laboratory of Aerosol Chemistry and Physics, State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China.
Junji CaoKey Laboratory of Aerosol Chemistry and Physics, State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China.
Jing WangInstitute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland. jing.wang@ifu.baug.ethz.ch.ORCID http://orcid.org/0000-0003-2078-137X
ETH Zurich · CHUniversity of Zurich · CHChinese Academy of Sciences · CNLiaocheng University · CNShaanxi Normal University · CN

Funding

China Scholarship Council 201706010410China Scholarship Council 201906875037Fundamental Research Funds for the Central Universities GK201601009Fundamental Research Funds for the Central Universities GK201603075Fundamental Research Funds for the Central Universities GK201802108National Natural Science Foundation of China 52000127Natural Science Foundation of Shaanxi Province 2017JQ5074the Science and Technology Project of Xi'an 2017071CG/ RC034(SXSF002)
6 · The paper itself

Abstract

The pandemic of coronavirus disease 2019 (COVID-19) continues to threaten public health. For developing countries where vaccines are still in shortage, cheaper alternative molecular methods for SARS-CoV-2 identification can be crucial to prevent the next wave. Therefore, 14 primer sets recommended by the World Health Organization (WHO) was evaluated on testing both clinical patient and environmental samples with the gold standard diagnosis method, TaqMan-based RT-qPCR, and a cheaper alternative method, SYBR Green-based RT-qPCR. Using suitable primer sets, such as ORF1ab, 2019_nCoV_N1 and 2019_nCoV_N3, the performance of the SYBR Green approach was comparable or better than the TaqMan approach, even when considering the newly dominating or emerging variants, including Delta, Eta, Kappa, Lambda, Mu, and Omicron. ORF1ab and 2019_nCoV_N3 were the best combination for sensitive and reliable SARS-CoV-2 molecular diagnostics due to their high sensitivity, specificity, and broad accessibility. KEY POINTS: • With suitable primer sets, the SYBR Green method performs better than the TaqMan one. • With suitable primer sets, both methods should still detect the new variants well. • ORF1ab and 2019_nCoV_N3 were the best combination for SARS-CoV-2 detection.

Indexed as

COVID-19SARS-CoV-2BenzothiazolesDiaminesHumansQuinolinesReal-Time Polymerase Chain ReactionRNA, ViralSensitivity and SpecificityBenzothiazolesDiaminesQuinolinesRNA, ViralSYBR Green ICOVID-19RT-qPCRSARS-CoV-2SYBR GreenTaqMan probe

Identifiers

PMID35218386
PMCPMC8881549
OpenAlexW4214771812

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.