Evidence map›Paper›PMID 41486394›Full record

ReviewNano-micro letters2026

Next-Generation Joint-on-a-Chip: Toward Precision Mechanical Control in Multi-Tissue Systems.

Zhenjun Lv, Yuwei Chai, Xiumei Zhang, Weiwei Lan, Junchao Wei, Lu Li, Weiyi Chen, Yiting Lei, Jun Liu, Zhong Alan Li and 1 more

Abstract readReview
In one paragraph

Review in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  3. Review
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  8. 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

11 authors.

Zhenjun LvDepartment of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China.
Yuwei ChaiDepartment of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China.
Xiumei ZhangDepartment of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China.
Weiwei LanDepartment of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China. bme7506@163.com.
Junchao WeiShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Second Hospital of Shanxi Medical University, Taiyuan, 030024, People's Republic of China.
Lu LiShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Second Hospital of Shanxi Medical University, Taiyuan, 030024, People's Republic of China.
Weiyi ChenDepartment of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China.
Yiting LeiDepartment of Biomedical Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, People's Republic of China.
Jun LiuDepartment of Biomedical Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, People's Republic of China.
Zhong Alan LiDepartment of Biomedical Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, People's Republic of China. alanli@cuhk.edu.hk.
Di HuangDepartment of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, People's Republic of China. huangjw2067@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis is among the leading causes of disability worldwide, and no pharmacological therapies currently exist to reverse its progression. This lack of therapies is primarily attributed to the inadequacies of conventional in vitro models of joint physiology and pathology, which significantly hinder advancements in disease mechanism research and drug development. As an emerging in vitro joint model, joint-on-a-chip (JoC) technology allows low-cost, efficient simulation of physiological and pathological joint activities, making it a focal point of current research. Cartilage, subchondral bone, and synovium are among the key tissues required for constructing in vitro joint models, with cartilage playing a central load-bearing role in joint movement. This article provides a detailed overview of the structure and function of these tissues, with an emphasis on the load-bearing mechanisms of cartilage, and identifies the microenvironmental characteristics that JoC should aim to replicate. Subsequently, we review the current types of JoC and highlight their core challenge: the seamless integration of multi-tissue co-culture with specific mechanical stimulation. To address this issue, we propose potential solutions and present a conceptual design for a JoC prototype. Finally, we discuss the challenges and issues related to the outlook for JoC. Our ultimate goal is to develop a JoC capable of replicating the key microenvironments of joints, serving as a high-performance in vitro joint model to advance the study of disease mechanisms and facilitate drug development.

Indexed as

Joint-on-a-chipMechanical stimulationMulti-tissue co-cultureOsteoarthritisTissue microenvironment

Identifiers

PMID41486394
PMCPMC12765799

What Socratic holds

Textmetadata
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 Socratic graph.