Evidence map›Paper›PMID 35735518›Full record

ReviewBiosensors2022

Microfluidic Organ-on-a-Chip System for Disease Modeling and Drug Development.

Zening Li, Jianan Hui, Panhui Yang, Hongju Mao

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

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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

4 authors.

Zening LiState Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.ORCID 0000-0002-5407-6065
Jianan HuiState Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
Panhui YangState Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.ORCID 0000-0003-0465-3490
Hongju MaoState Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.

Funding

China Postdoctoal Science Foundation 2020000246Grants from National Key Research and Development Program, China No.2018YFA0108202National Science Foundation, China No.61971410 and 62001458Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine 20DZ2254400Shanghai Sailing Program No.20YF1457100
6 · The paper itself

Abstract

An organ-on-a-chip is a device that combines micro-manufacturing and tissue engineering to replicate the critical physiological environment and functions of the human organs. Therefore, it can be used to predict drug responses and environmental effects on organs. Microfluidic technology can control micro-scale reagents with high precision. Hence, microfluidics have been widely applied in organ-on-chip systems to mimic specific organ or multiple organs in vivo. These models integrated with various sensors show great potential in simulating the human environment. In this review, we mainly introduce the typical structures and recent research achievements of several organ-on-a-chip platforms. We also discuss innovations in models applied to the fields of pharmacokinetics/pharmacodynamics, nano-medicine, continuous dynamic monitoring in disease modeling, and their further applications in other fields.

Indexed as

Lab-On-A-Chip DevicesMicrofluidicsDrug DevelopmentHumansTissue Engineeringdisease modelingdrug developmentmicrofluidicsorgan-on-a-chip

Identifiers

PMID35735518
PMCPMC9220862

What OpenQuestion holds

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Registered trials

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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.