Evidence map›Paper›PMID 31476281›Full record

ReviewTranslational research : the journal of laboratory and clinical medicine2019

siRNA- and miRNA-based therapeutics for liver fibrosis.

Zhen Zhao, Chien-Yu Lin, Kun Cheng

Open access · greenAbstract readReview
In one paragraph

Review in Translational research : the journal of laboratory and clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
6.3field-weighted citation impact, top 3% 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

51 citing papers in PubMed, 103 citations in OpenAlex.

  1. Review
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  9. Sinusoidal communication in chronic liver disease.Clinical and molecular hepatology · 2025
    Review
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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 at 1 institution in 1 country.

Zhen ZhaoDivision of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri.
Chien-Yu LinDivision of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri.
Kun ChengDivision of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri. Electronic address: chengkun@umkc.edu.
University of Missouri–Kansas City · US

Funding

Targeted delivery of PCBP2 siRNA for treating alcoholic liver fibrosisR01AA021510 · NIAAA · UNIVERSITY OF MISSOURI KANSAS CITY · PI CHENG, KUN · 2012 to 2022
$3.8M
NIAAA NIH HHS R01 AA021510
6 · The paper itself

Abstract

Liver fibrosis is a wound-healing process induced by chronic liver injuries, such as nonalcoholic steatohepatitis, hepatitis, alcohol abuse, and metal poisoning. The accumulation of excessive extracellular matrix (ECM) in the liver is a key characteristic of liver fibrosis. Activated hepatic stellate cells (HSCs) are the major producers of ECM and therefore play irreplaceably important roles during the progression of liver fibrosis. Liver fibrogenesis is highly correlated with the activation of HSCs, which is regulated by numerous profibrotic cytokines. Using RNA interference to downregulate these cytokines in activated HSCs is a promising strategy to reverse liver fibrosis. Meanwhile, microRNAs (miRNAs) have also been exploited for the treatment of liver fibrosis. This review focuses on the current siRNA- and miRNA-based liver fibrosis treatment strategies by targeting activated HSCs in the liver.

Indexed as

AnimalsHumansLiver CirrhosisMicroRNAsModels, BiologicalRNA, Small InterferingSignal TransductionMicroRNAsRNA, Small Interfering

Identifiers

PMID31476281
PMCPMC6848786
OpenAlexW2969046382

What OpenQuestion holds

Textmetadata
LicenceTDM
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.