Evidence map›Paper›PMID 34898050›Full record

ArticleClinical and translational medicine2021

How to translate the knowledge of COVID-19 into the prevention of Omicron variants.

Xiangdong Wang, Charles A Powell

Open access · goldAbstract readEditorial
In one paragraph

Article in Clinical and translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.0field-weighted citation impact, top 11% 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

18 citing papers in PubMed, 30 citations in OpenAlex.

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  12. Clinical and translational values of spatial transcriptomics.Signal transduction and targeted therapy · 2022
    Review
  13. SARS-CoV-2-Specific Immune Response and the Pathogenesis of COVID-19.International journal of molecular sciences · 2022
    Review
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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

2 authors at 2 institutions in 2 countries.

Xiangdong WangDepartment of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0000-0002-8406-7928
Charles A PowellDivision of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Icahn School of Medicine at Mount Sinai · USShanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Omicron variants are part of the "Coronavirus disease 2019 [COVID-19] Variants of Concerns" and has the potential to spread around the world rapidly and can harm human life. We can anticipate that the endemic state of COVID-19 will be characterized by the development of new strains with surges that will predominate in unvaccinated and immunodeficient populations. Thus, there will be an important role in promoting vaccinations, boosters and accessible testing to prevent disease transmission and to rapidly detect surges. There is an urgent need to explore the virology and biology of Omicron variants, define clinical phenomes and therapies, monitor dynamics of genetic changes, and translate the knowledge of COVID-19 into new variants. Clinical and translational medicine will be impactful in addressing these challenges by providing new insights for understanding and predicting new variants-associated transmissibility, disease severity, immune escape, diagnostic or therapeutic failure.

Indexed as

COVID-19COVID-19 VaccinesHumansImmune SystemMutationSARS-CoV-2South AfricaTranslational Research, BiomedicalTranslational Science, BiomedicalVaccinationCOVID-19 VaccinesCOVID-19diseaseOmicron variantspreventiontherapy

Identifiers

PMID34898050
PMCPMC8666580
OpenAlexW4200537657

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.