Evidence map›Paper›PMID 37397560›Full record

ReviewGenes & diseases2023

Interplays of liver fibrosis-associated microRNAs: Molecular mechanisms and implications in diagnosis and therapy.

Hong Li, Tingli Liu, Yongchun Yang, William C Cho, Robin J Flynn, Majid Fasihi Harandi, Houhui Song, Xuenong Luo, Yadong Zheng

Abstract readReview
In one paragraph

Review in Genes & diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Recent advances in biomarkers for cardiac fibrosis.Frontiers in cardiovascular medicine · 2026
    Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Therapeutic Applications of Poly-miRNAs and miRNA Sponges.International journal of molecular sciences · 2025
    Review
  17. Review
  18. Review
  19. Predicting hepatitis C infection via machine learning.American journal of translational research · 2025
    Article
  20. 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

9 authors.

Hong LiState Key Laboratory of Veterinary Etiological Biology, Key Laboratory of Veterinary Parasitology of Gansu Province, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu 730046, China.
Tingli LiuState Key Laboratory of Veterinary Etiological Biology, Key Laboratory of Veterinary Parasitology of Gansu Province, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu 730046, China.
Yongchun YangKey Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang 311300, China.
William C ChoDepartment of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR 999077, China.
Robin J FlynnDepartment of Infection Biology, Institute of Infection and Global Health, University of Liverpool, Liverpool L3 5RF, UK.
Majid Fasihi HarandiResearch Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman 7616914115, Iran.
Houhui SongKey Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang 311300, China.
Xuenong LuoState Key Laboratory of Veterinary Etiological Biology, Key Laboratory of Veterinary Parasitology of Gansu Province, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, Gansu 730046, China.
Yadong ZhengKey Laboratory of Applied Technology on Green-Eco-Healthy Animal Husbandry of Zhejiang Province, Zhejiang Provincial Engineering Laboratory for Animal Health Inspection & Internet Technology, Zhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health Management, China-Australia Joint Laboratory for Animal Health Big Data Analytics, College of Animal Science and Technology & College of Veterinary Medicine of Zhejiang A&F University, Hangzhou, Zhejiang 311300, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

microRNAs (miRNAs) are a class of non-coding functional small RNA composed of 21-23 nucleotides, having multiple associations with liver fibrosis. Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis types. The former is capable of activating hepatic stellate cells (HSCs) by modulating pro-fibrotic signaling pathways, mainly including TGF-β/SMAD, WNT/β-catenin, and Hedgehog; the latter is responsible for maintenance of the quiescent phenotype of normal HSCs, phenotypic reversion of activated HSCs (aHSCs), inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene expression. Moreover, several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms, such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of aHSCs. Thus, understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.

Indexed as

DiagnosisHepatic stellate cellLiver fibrosismiRNATherapy

Identifiers

PMID37397560
PMCPMC10311052

What OpenQuestion holds

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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.