Evidence map›Paper›PMID 41893739›Full record

ReviewVaccines2026

From Design to Clinical Use: mRNA Vaccines for Infectious Diseases and Cancer.

Yulin Cui, Ziyue Liang, Hua Cong

Abstract readReview
In one paragraph

Review in Vaccines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Yulin CuiDepartment of Pathogenic Biology, School of Basic Medicine, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
Ziyue LiangDepartment of Pathogenic Biology, School of Basic Medicine, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
Hua CongDepartment of Pathogenic Biology, School of Basic Medicine, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

mRNA vaccines represent a revolutionary advance in vaccinology, boasting advantages like rapid development, robust immunogenicity and flexible antigen design over traditional vaccines. This review systematically summarizes the core research progress of mRNA vaccines, including their structural composition with five functional elements and novel subtypes (linear mRNA, self-amplifying RNA, circular RNA) with unique biological characteristics and application value. It elaborates on the immune activation mechanism of mRNA vaccines, which mimic natural viral infection to trigger both innate and adaptive immunity, and analyzes mainstream delivery systems (lipid nanoparticles, dendritic cells, protamine, exosomes, polymers) with their respective performance, advantages and bottlenecks. This review also details the clinical application status of mRNA vaccines in infectious diseases (influenza, rabies, monkeypox, SARS-CoV-2, HIV, parasites) and cancer therapy, highlighting promising preclinical and clinical results of candidate vaccines and combined therapeutic regimens. Additionally, it addresses the current limitations of mRNA vaccines, such as delivery inefficiency, production costs, and cold chain constraints. Finally, this review prospects the future development direction, emphasizing that the optimization of delivery systems, antigen design and production processes will further promote the clinical translation and diversified application of mRNA vaccines in disease prevention and treatment.

Indexed as

cancer immunotherapycircular RNA vaccineinfectious diseaselipid nanoparticle (LNP)mRNA vaccinevaccine delivery system

Identifiers

PMID41893739
PMCPMC13030624

What OpenQuestion holds

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LicenceCC BY
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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.