Evidence map›Paper›PMID 40059951›Full record

ReviewJournal of inflammation research2025

RNA-Based Therapies in Kidney Diseases.

Liang Hu, Ting Jin, Ning Zhang, Jin Ding, Lina Li

Abstract readReview
In one paragraph

Review in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

5 authors.

Liang Hu *Department of Urology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, 321001, People's Republic of China.
Ting Jin *Department of Gastroenterology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, 321001, People's Republic of China.
Ning ZhangCollege of Medicine, Jinhua University of Vocational Technology, Jinhua, Zhejiang, 321016, People's Republic of China.
Jin DingDepartment of Gastroenterology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, 321001, People's Republic of China.
Lina LiCollege of Medicine, Jinhua University of Vocational Technology, Jinhua, Zhejiang, 321016, People's Republic of China.ORCID 0000-0002-8662-6885

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney diseases are major global health challenges, affecting over 750 million people worldwide. Despite significant efforts, effective treatment strategies are still insufficient. In recent years, RNA therapeutics have made substantial progress, and an increasing number of nucleic acid-based therapies have been approved, showing potential for treating various diseases (including kidney diseases). These therapies can target proteins, transcripts, and genes that were previously considered "undruggable", allowing for the regulation of their expression and the expansion of therapeutic targets. Among RNA therapeutics, mRNA-based therapies are particularly promising because they can rapidly express therapeutic proteins, along with their design flexibility and potential to target previously inaccessible disease mechanisms. This review discussed various RNA-based strategies for developing new treatments, including antisense and RNA interference technologies, mRNA-based approaches, and CRISPR-Cas-mediated genome editing. Additionally, we highlighted the opportunities and challenges associated with the widespread application of these therapies in kidney disease treatment.

Indexed as

kidney diseasesmRNA therapyRNA-based therapies

Identifiers

PMID40059951
PMCPMC11890006

What OpenQuestion holds

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LicenceCC BY-NC
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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.