Evidence map›Paper›PMID 35625364›Full record

ReviewBiology2022

Pathological Contribution of Extracellular Vesicles and Their MicroRNAs to Progression of Chronic Liver Disease.

Chanbin Lee, Jinsol Han, Youngmi Jung

Open access · goldAbstract readReview
In one paragraph

Review in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

11 citing papers in PubMed, 14 citations in OpenAlex.

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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.

Chanbin LeeInstitute of Systems Biology, College of Natural Science, Pusan National University, Pusan 46241, Korea.ORCID 0000-0002-5680-9613
Jinsol HanDepartment of Integrated Biological Science, College of Natural Science, Pusan National University, Pusan 46241, Korea.ORCID 0000-0002-5317-9409
Youngmi JungDepartment of Integrated Biological Science, College of Natural Science, Pusan National University, Pusan 46241, Korea.ORCID 0000-0003-2189-0658
Pusan National University · KR

Funding

National Research Foundation of Korea No. 2022R1C1C2008830
6 · The paper itself

Abstract

Extracellular vesicles (EVs) are membrane-bound endogenous nanoparticles released by the majority of cells into the extracellular space. Because EVs carry various cargo (protein, lipid, and nucleic acids), they transfer bioinformation that reflects the state of donor cells to recipient cells both in healthy and pathologic conditions, such as liver disease. Chronic liver disease (CLD) affects numerous people worldwide and has a high mortality rate. EVs released from damaged hepatic cells are involved in CLD progression by impacting intercellular communication between EV-producing and EV-receiving cells, thereby inducing a disease-favorable microenvironment. In patients with CLD, as well as in the animal models of CLD, the levels of released EVs are elevated. Furthermore, these EVs contain high levels of factors that accelerate disease progression. Therefore, it is important to understand the diverse roles of EVs and their cargoes to treat CLD. Herein, we briefly explain the biogenesis and types of EVs and summarize current findings presenting the role of EVs in the pathogenesis of CLD. As the role of microRNAs (miRNAs) within EVs in liver disease is well documented, the effects of miRNAs detected in EVs on CLD are reviewed. In addition, we discuss the therapeutic potential of EVs to treat CLD.

Indexed as

chronic liver diseaseexosomesextracellular vesiclesmicroRNA

Identifiers

PMID35625364
PMCPMC9137620
OpenAlexW4224263463

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.