Evidence map›Paper›PMID 38675814›Full record

ArticleVaccines2024

Enhancement of SARS-CoV-2 mRNA Vaccine Efficacy through the Application of TMSB10 UTR for Superior Antigen Presentation and Immune Activation.

Xiaoyan Ding, Yuxin Zhou, Jiuxiang He, Jing Zhao, Jintao Li

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2024. 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
1.9field-weighted citation impact, top 15% 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, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

5 authors at 1 institution in 2 countries.

Xiaoyan DingCollege of Basic Medicine, Third Military Medical University, Chongqing 400038, China.
Yuxin ZhouCollege of Basic Medicine, Third Military Medical University, Chongqing 400038, China.
Jiuxiang HeCollege of Basic Medicine, Third Military Medical University, Chongqing 400038, China.
Jing ZhaoCollege of Basic Medicine, Third Military Medical University, Chongqing 400038, China.
Jintao LiCollege of Basic Medicine, Third Military Medical University, Chongqing 400038, China.
Army Medical University · CN

Funding

Natural Science Foundation Project of Chongqing cstc2020jcyj-bshX0116
6 · The paper itself

Abstract

The development of effective vaccines against SARS-CoV-2 remains a critical challenge amidst the ongoing global pandemic. This study introduces a novel approach to enhancing mRNA vaccine efficacy by leveraging the untranslated region (UTR) of TMSB10, a gene identified for its significant mRNA abundance in antigen-presenting cells. Utilizing the GEO database, we identified TMSB10 among nine genes, with the highest mRNA abundance in dendritic cell subtypes. Subsequent experiments revealed that TMSB10's UTR significantly enhances the expression of a reporter gene in both antigen-presenting and 293T cells, surpassing other candidates and a previously optimized natural UTR. A comparative analysis demonstrated that TMSB10 UTR not only facilitated a higher reporter gene expression in vitro but also showed marked superiority in vivo, leading to enhanced specific humoral and cellular immune responses against the SARS-CoV-2 Delta variant RBD antigen. Specifically, vaccines incorporating TMSB10 UTR induced significantly higher levels of specific IgG antibodies and promoted a robust T-cell immune response, characterized by the increased secretion of IFN-γ and IL-4 and the proliferation of CD4

Indexed as

antigen-presenting cellsmRNA vaccineSARS-CoV-2UTR

Identifiers

PMID38675814
PMCPMC11053782
OpenAlexW4394892235

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.