Evidence map›Paper›PMID 42016750›Full record

ReviewExploration (Beijing, China)2026

Microengineering the Liver: Strategies for Constructing Functional Liver-on-a-Chip Devices.

Jie Wang, Ziwei Liang, Jiapu Wang, Zongyi Li, Shaojie Wang, Yan Wei, Xin Xie, Di Huang

Abstract readReview
In one paragraph

Review in Exploration (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Jie WangDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.
Ziwei LiangDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.
Jiapu WangDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.ORCID https://orcid.org/0000-0001-8852-1019
Zongyi LiDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.
Shaojie WangDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.
Yan WeiDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.
Xin XieXellar-Biosystems Cambridge Massachusetts USA.
Di HuangDepartment of Biomedical Engineering Research Center for Nano-Biomaterials & Regenerative Medicine College of Artificial Intelligence Shanxi Key Laboratory of Materials Strength & Structural Impact Taiyuan University of Technology Taiyuan China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reliable in vitro liver models are indispensable for researching liver diseases and developing medications. Present 2D/3D cell cultures and animal models inadequately replicate the intricacy of living systems and in vivo conditions, resulting in impaired cellular functions. They also fail to emulate tissue-like architectures, which undermines their precision. Meanwhile, animal models present species differences, making real-time observation of dynamic results inconvenient and raising serious ethical concerns. Therefore, there is an urgent need to develop alternative tissue models with biomimetic human pathophysiology to bridge the gap between clinical trials and traditional human and animal models. Liver-on-a-chip (LOC) technology, based on microfluidics, is an innovative in vitro modeling device that can replicate the microstructures and tissue-tissue interfaces of specific liver functional units, simulating organ and tissue-level physiological activities. This review summarizes recent strategies and breakthroughs in LOC technologies, from biomimetic tissues and extracellular matrix construction in liver microphysiological systems to diverse LOC development approaches. Furthermore, we highlight key advances in functional LOC platforms, including 3D bioprinting, vascularization strategies, and the incorporation of liver buds and organoids to enhance physiological relevance. The integration of deep learning and sensor technologies for intelligent, real-time monitoring is also discussed. Finally, we examine LOC applications in drug screening and disease modeling, assess challenges in clinical translation, and offer perspectives on future directions in biomedical research and personalized medicine.

Indexed as

deep learningdisease modelingdrug screeningliver‐on‐a‐chipmicrophysiological systems

Identifiers

PMID42016750
PMCPMC13094531

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.