Evidence map›Paper›PMID 36298546›Full record

ArticleVaccines2022

Using MicroRNA Arrays as a Tool to Evaluate COVID-19 Vaccine Efficacy.

Yen-Pin Lin, Yi-Shan Hsieh, Mei-Hsiu Cheng, Ching-Fen Shen, Ching-Ju Shen, Chao-Min Cheng

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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  4. Review
  5. Review
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.

Yen-Pin LinInstitute of Biomedical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan.
Yi-Shan HsiehTaiwan Business Development Department, Inti Taiwan, Inc., Hsinchu 302, Taiwan.
Mei-Hsiu ChengTaiwan Business Development Department, Inti Taiwan, Inc., Hsinchu 302, Taiwan.
Ching-Fen ShenDepartment of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan.ORCID 0000-0001-5926-0462
Ching-Ju ShenDepartment of Obstetrics and Gynecology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0002-9975-4210
Chao-Min ChengInstitute of Biomedical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan.ORCID 0000-0002-8644-1960
Kaohsiung Medical University · TWNational Tsing Hua University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to solve COVID-19 pandemic, the entire world has invested considerable manpower to develop various new vaccines to temporarily alleviate the disaster caused by the epidemic. In addition to the development of vaccines, we need to also develop effective assessment methods to confirm vaccines' efficacy and maximize the benefits that vaccines can bring. In addition to common evaluation methods, vaccine-specific and temporal expression of microRNAs have been shown to be related to vaccine efficacy or vaccine-associated diseases. In this article, we have introduced a microRNA-array-based approach, which could be potentially used for evaluating COVID-19 vaccine efficacy, specifically for pregnant women. As the mRNA in mRNA vaccines is decomposed by host cells within a few days, it is considered more suitable for pregnant women to utilize the method of vaccination during pregnancy. Moreover, pregnant women belong to a high-risk group for COVID-19, and there is currently no appropriate vaccine to newborns. Therefore, it's important to find improved tools for evaluation of vaccine efficacy in response to the current situation caused by COVID-19.

Indexed as

COVID-19immune responsemicroRNA arraymRNA vaccinepregnancy

Identifiers

PMID36298546
PMCPMC9607069
OpenAlexW4304189948

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.