Evidence map›Paper›PMID 34819926›Full record

ReviewFrontiers in microbiology2021

An Update on Animal Models for Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Countermeasure Development.

Liang Zhang, Shuaiyin Chen, Weiguo Zhang, Haiyan Yang, Yuefei Jin, Guangcai Duan

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 41% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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 at 2 institutions in 1 country.

Liang ZhangDepartment of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Shuaiyin ChenDepartment of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Weiguo ZhangDepartment of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Haiyan YangDepartment of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yuefei JinDepartment of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Guangcai DuanDepartment of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China.
Zhengzhou University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic since March 2020 and led to significant challenges to over 200 countries and regions all over the world. The establishment of highly pathogenic coronavirus animal model is beneficial for the study of vaccines and pathogenic mechanism of the virus. Laboratory mice, Syrian hamsters, Non-human primates and Ferrets have been used to establish animal models of emerging coronavirus infection. Different animal models can reproduce clinical infection symptoms at different levels. Appropriate animal models are of great significance for the pathogenesis of COVID-19 and the research progress related to vaccines. This review aims to introduce the current progress about experimental animal models for SARS-CoV-2, and collectively generalize critical aspects of disease manifestation in humans and increase their usefulness in research into COVID-19 pathogenesis and developing new preventions and treatments.

Indexed as

ACE2animal modelCOVID-19infectionSARS-CoV-2

Identifiers

PMID34819926
PMCPMC8606789
OpenAlexW3214105194

What Socratic 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 Socratic graph.