Evidence map›Paper›PMID 42688653›Full record

ReviewResearch (Washington, D.C.)2026

Unveiling Particulate Matter-Lung Interactions from Static Models to Dynamic Lung-on-Chips.

Yixin Ren, Xi Deng, Mengqi Yang, Ziwei Liang, Yan Wei, Ting Liu, Weiwei Lan, Yiwei Shi, Di Huang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Research (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yixin RenDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.
Xi DengDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.
Mengqi YangDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.
Ziwei LiangDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.
Yan WeiDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.
Ting LiuNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan 030001, China.
Weiwei LanDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.
Yiwei ShiNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan 030001, China.
Di HuangDepartment of Biomedical Engineering, Research Center for Nano-biomaterials & Regenerative Medicine, Shanxi Key Laboratory of Functional Proteins, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan 030024, China.ORCID https://orcid.org/0000-0001-8123-9766

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pervasive inhalable particulate matter (PM) in the environment poses a marked threat to pulmonary health. However, the underlying pathogenic mechanisms remain poorly understood, largely due to the limitations of existing in vitro models in accurately mimicking the complex biological processes. This review critically examines the spectrum of in vitro models employed in PM research, spanning from traditional 2-dimensional models to advanced 3-dimensional (3D) constructs, including 3D bioprinted models and organoids, as well as the burgeoning field of lung-on-chips (LOCs). Each model type is meticulously assessed for its unique strengths and the challenges. A particular emphasis is placed on the innovative use of LOCs technology to replicate the dynamic nature of respiratory motion. The review delves into how integrating breath-mimicking stimuli can modulate the intricate interactions between PM and cellular/tissue interfaces. Ultimately, it unveils an innovative, highly integrated, and intelligent LOCs platform, offering a forward-looking insight into its evolution.

Identifiers

PMID42688653
PMCPMC13534807

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.