Evidence map›Paper›PMID 34766158›Full record

ReviewView (Beijing, China)2021

Applications of digital PCR in COVID-19 pandemic.

Chianru Tan, Dongdong Fan, Nan Wang, Fang Wang, Bo Wang, Lingxiang Zhu, Yong Guo

Open access · goldAbstract readReview
In one paragraph

Review in View (Beijing, China), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.5field-weighted citation impact, top 6% 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

22 citing papers in PubMed, 60 citations in OpenAlex.

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  10. Efficient Metabolic Fingerprinting of Follicular Fluid Encodes Ovarian Reserve and Fertility.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
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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

7 authors at 2 institutions in 1 country.

Chianru TanDepartment of Biomedical Engineering, School of Medicine Tsinghua University Beijing China.ORCID https://orcid.org/0000-0002-9032-6746
Dongdong FanDepartment of Biomedical Engineering, School of Medicine Tsinghua University Beijing China.
Nan WangDepartment of Biomedical Engineering, School of Medicine Tsinghua University Beijing China.
Fang WangDepartment of Biomedical Engineering, School of Medicine Tsinghua University Beijing China.
Bo WangTargetingOne Corporation Beijing China.
Lingxiang ZhuNational Research Institute for Family Planning Beijing China.
Yong GuoDepartment of Biomedical Engineering, School of Medicine Tsinghua University Beijing China.ORCID https://orcid.org/0000-0003-3175-3008
Tsinghua University · CNTianjin Municipal Research Institute for Family Planning · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has led to a public health crisis and global panic. This infectious disease is caused by a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Digital polymerase chain reaction (dPCR), which is an emerging nucleic acid amplification technology that allows absolute quantification of nucleic acids, plays an important role in the detection of SARS-CoV-2. In this review, we introduce the principle and advantages of dPCR, and review the applications of dPCR in the COVID-19 pandemic, including detection of low copy number viruses, measurement of the viral load, preparation of reference materials, monitoring of virus concentration in the environment, detection of viral mutations, and evaluation of anti-SARS-CoV-2 drugs. We also discuss the challenges of dPCR in clinical practice.

Indexed as

COVID‐19digital PCRnucleic acid testingSARS‐CoV‐2

Identifiers

PMID34766158
PMCPMC7883284
OpenAlexW3118769644

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.