Evidence map›Paper›PMID 40710256›Full record

ReviewBiomimetics (Basel, Switzerland)2025

Current Advances and Future Perspectives of Liver-on-a-Chip Platforms Incorporating Dynamic Fluid Flow.

Jingyeong Yun, Tae-Joon Jeon, Sun Min Kim

Abstract readReview
In one paragraph

Review in Biomimetics (Basel, Switzerland), 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

3 authors.

Jingyeong YunDepartment of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea.ORCID 0009-0004-3153-802X
Tae-Joon JeonDepartment of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea.ORCID 0000-0002-4882-9040
Sun Min KimDepartment of Biological Sciences and Bioengineering, Inha University, Incheon 22212, Republic of Korea.ORCID 0000-0001-8420-637X

Funding

Inha University 2025
6 · The paper itself

Abstract

The liver is a vital organ responsible for a broad range of metabolic functions, including glucose and lipid metabolism, detoxification, and protein synthesis. Its structural complexity, characterized by hexagonal hepatic lobules composed of diverse parenchymal and non-parenchymal cell types, supports its broad spectrum of physiological activities. Traditional in vitro liver models have contributed significantly to our understanding of hepatic biology and the development of therapies for liver-related diseases. However, static culture systems fail to replicate the dynamic in vivo microenvironment, particularly the continuous blood flow and shear stress that are critical for maintaining hepatocyte function and metabolic zonation. Recent advances in microphysiological systems (MPS) incorporating dynamic fluid flow have addressed these limitations by providing more physiologically relevant platforms for modeling liver function. These systems offer improved fidelity for applications in drug screening, toxicity testing, and disease modeling. Furthermore, the integration of liver MPS with other organ models in multi-organ-on-chip platforms has enabled the investigation of inter-organ crosstalk, enhancing the translational potential of in vitro systems. This review summarizes recent progress in the development of dynamic liver MPS, highlights their biomedical applications, and discusses future directions for creating more comprehensive and predictive in vitro models.

Indexed as

disease modelingdynamic fluid flowliver-on-a-chipmicrophysiological systemsmulti-organ-on-a-chip

Identifiers

PMID40710256
PMCPMC12293049

What OpenQuestion holds

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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.