Evidence map›Paper›PMID 35572592›Full record

SynthesisFrontiers in immunology2022

Vaccines for COVID-19: A Systematic Review of Immunogenicity, Current Development, and Future Prospects.

Zhan Zhang, Qi Shen, Haocai Chang

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–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

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

3 authors.

Zhan ZhangMinistry of Education (MOE) Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China.
Qi ShenMinistry of Education (MOE) Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China.
Haocai ChangMinistry of Education (MOE) Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The persistent coronavirus disease 2019 (COVID-19), characterized by severe respiratory syndrome, is caused by coronavirus 2 (SARS-CoV-2), and it poses a major threat to public health all over the world. Currently, optimal COVID-19 management involves effective vaccination. Vaccination is known to greatly enhance immune response against viral infections and reduce public transmission of COVID-19. However, although current vaccines offer some benefits, viral variations and other factors demand the continuous development of vaccines to eliminate this virus from host. Hence, vaccine research and development is crucial and urgent to the elimination of this pandemic. Herein, we summarized the structural and replicatory features of SARS-CoV-2, and focused on vaccine-mediated disease prevention strategies like vaccine antigen selection, vaccine research, and vaccine application. We also evaluated the latest literature on COVID-19 and extensively reviewed action mechanisms, clinical trial (CT) progresses, advantages, as well as disadvantages of various vaccine candidates against SARS-CoV-2. Lastly, we discussed the current viral treatment, prevention trends, and future prospects.

Indexed as

COVID-19VaccinesCOVID-19 VaccinesHumansPandemicsSARS-CoV-2COVID-19 VaccinesVaccinesantigen selectionCOVID-19COVID-19 vaccinespreventionSARS-CoV-2

Identifiers

PMID35572592
PMCPMC9092649

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.