Evidence map›Paper›PMID 34821672›Full record

ReviewBiosensors2021

Microfluidic-Chip-Integrated Biosensors for Lung Disease Models.

Shuang Ding, Haijun Zhang, Xuemei Wang

Abstract readReview
In one paragraph

Review in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. A practical toolbox for modelling fibrosis in vitro.Nature biomedical engineering · 2026
    Review
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  8. Frontiers in allergy · 2025
    Review
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  11. On the path to predicting immune responses in the lung: Modeling the pulmonary innate immune system at the air-liquid interface (ALI).European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2023
    Review
  12. Article
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  14. Review
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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.

Shuang DingDepartment of Oncology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
Haijun ZhangDepartment of Oncology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China.ORCID 0000-0002-4313-3001
Xuemei WangState Key Laboratory of Bioelectronics, School of Biomedical Engineering, Southeast University, Nanjing 210096, China.

Funding

Fundamental Research Funds for the Central Universities and the Ordinary University Graduate Student Research Innovation Project of Jiangsu Province,China KYCX18_0185Science and Technology Grants of Jiangsu Province BE2019716the Science and Technology Grants of Jiangsu Province BE2019738
6 · The paper itself

Abstract

Lung diseases (e.g., infection, asthma, cancer, and pulmonary fibrosis) represent serious threats to human health all over the world. Conventional two-dimensional (2D) cell models and animal models cannot mimic the human-specific properties of the lungs. In the past decade, human organ-on-a-chip (OOC) platforms-including lung-on-a-chip (LOC)-have emerged rapidly, with the ability to reproduce the in vivo features of organs or tissues based on their three-dimensional (3D) structures. Furthermore, the integration of biosensors in the chip allows researchers to monitor various parameters related to disease development and drug efficacy. In this review, we illustrate the biosensor-based LOC modeling, further discussing the future challenges as well as perspectives in integrating biosensors in OOC platforms.

Indexed as

Biosensing TechniquesLab-On-A-Chip DevicesLung DiseasesMicrofluidicsAnimalsHumansbiosensorlung modellung-on-a-chipmicrofluidicsorgan-on-a-chip

Identifiers

PMID34821672
PMCPMC8615803

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.