Evidence map›Paper›PMID 35679788›Full record

ArticleWater research2022

Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance.

Xiaoqing Xu, Yu Deng, Jiahui Ding, Xiawan Zheng, Shuxian Li, Lei Liu, Ho-Kwong Chui, Leo L M Poon, Tong Zhang

Open access · greenAbstract read
In one paragraph

Article in Water research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.7field-weighted citation impact, top 8% 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

11 citing papers in PubMed, 28 citations in OpenAlex.

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  9. Advances in virus detection methods for wastewater-based epidemiological applications.Case studies in chemical and environmental engineering · 2022
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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

9 authors at 1 institution in 1 country.

Xiaoqing XuEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Yu DengEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Jiahui DingEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Xiawan ZhengEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Shuxian LiEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Lei LiuEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Ho-Kwong ChuiEnvironmental Protection Department, The Government of Hong Kong SAR, Tamar, Hong Kong SAR, China.
Leo L M PoonSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China.
Tong ZhangEnvironmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China. Electronic address: zhangt@hkucc.hku.hk.
University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage. In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances. Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.

Indexed as

COVID-19SARS-CoV-2AllelesHumansSewageSewageAllele-specific RT-qPCRReal-timeSARS-CoV-2Sewage surveillanceVariants

Identifiers

PMID35679788
PMCPMC9148393
OpenAlexW4281618992

What OpenQuestion holds

Textmetadata
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.