Evidence map›Paper›PMID 34433805›Full record

ReviewSignal transduction and targeted therapy2021

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19.

Zirui Meng, Shuo Guo, Yanbing Zhou, Mengjiao Li, Minjin Wang, Binwu Ying

Abstract readReview
In one paragraph

Review in Signal transduction and targeted therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

6 authors.

Zirui Meng *Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Shuo Guo *Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Yanbing ZhouDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Mengjiao LiDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Minjin WangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Binwu YingDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China. yingbinwu@scu.edu.cn.

Funding

Sichuan Provincial Youth Science and Technology Fund 2020YFS0004
6 · The paper itself

Abstract

The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

Indexed as

COVID-19 Drug TreatmentCOVID-19 TestingCOVID-19EpidemicsHumansSARS-CoV-2

Identifiers

PMID34433805
PMCPMC8386162

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.