Evidence map›Paper›PMID 42293248›Full record

ArticleMolecular therapy. Nucleic acids2026

A self-amplifying RNA vector based on rubella virus for mRNA therapeutics and vaccine applications.

Miao Jing, Michelle Cheok Yien Law, Cao Dai Phung, Yu He, Yaw Bia Tan, Minh T N Le, Dahai Luo

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Miao JingLee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore.
Michelle Cheok Yien LawLee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore.
Cao Dai PhungDepartment of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
Yu HeLee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore.
Yaw Bia TanLee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore.
Minh T N LeDepartment of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
Dahai LuoLee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significant efforts have been made in the burgeoning field of RNA therapies since the COVID-19 pandemic. Self-amplifying RNA (saRNA)-based therapies are considered more promising than conventional messenger RNA due to their lower concentration requirements, long-lasting effects, and self-adjuvanticity. Here, we developed a novel saRNA vector, based on the rubella virus ([RuV], based on the RA27/3 vaccine strain) as an alternative virus-derived replicon that demonstrates effective expression in multiple cell lines and induces immune response. As it is derived from a commonly used vaccine, it potentially offers a better safety profile with reduced risk of adverse events compared with current alphavirus-derived saRNAs. We further optimized this RuV saRNA by screening the capsid region, the nonstructural polyprotein p200 codon, and the 3' untranslated region. This RuV saRNA has great potential to be used as a vaccine vector and in other applications in RNA therapeutics.

Indexed as

mRNA therapeuticsMT: Oligonucleotides: Therapies and Applicationsrubella virusself-amplifying RNAvaccine

Identifiers

PMID42293248
PMCPMC13264025

What OpenQuestion holds

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Registered trials

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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.