Evidence map›Paper›PMID 39386741›Full record

ReviewFrontiers in medicine2024

Nanogene editing drug delivery systems in the treatment of liver fibrosis.

Qun Wang, Siyu Jia, Zihan Wang, Hui Chen, Xinyi Jiang, Yan Li, Peng Ji

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

7 authors.

Qun WangCollege of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou, China.
Siyu JiaCollege of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou, China.
Zihan WangCollege of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou, China.
Hui ChenCollege of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou, China.
Xinyi JiangCollege of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou, China.
Yan LiDepartment of International Medicine, The Second Hospital of Dalian Medical University, Dalian, China.
Peng JiCollege of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver fibrosis is a group of diseases that seriously affect the health of the world's population. Despite significant progress in understanding the mechanisms of liver fibrogenesis, the technologies and drugs used to treat liver fibrosis have limited efficacy. As a revolutionary genetic tool, gene editing technology brings new hope for treating liver fibrosis. Combining nano-delivery systems with gene editing tools to achieve precise delivery and efficient expression of gene editing tools that can be used to treat liver fibrosis has become a rapidly developing field. This review provides a comprehensive overview of the principles and methods of gene editing technology and commonly used gene editing targets for liver fibrosis. We also discuss recent advances in common gene editing delivery vehicles and nano-delivery formulations in liver fibrosis research. Although gene editing technology has potential advantages in liver fibrosis, it still faces some challenges regarding delivery efficiency, specificity, and safety. Future studies need to address these issues further to explore the potential and application of liver fibrosis technologies in treating liver fibrosis.

Indexed as

CRISPR-Cas9delivery systemgene editingliver fibrosisnanoparticlestargeted therapy

Identifiers

PMID39386741
PMCPMC11461213

What OpenQuestion holds

Textmetadata
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.