Evidence map›Paper›PMID 32825153›Full record

ReviewMicromachines2020

Microfluidics-Based Systems in Diagnosis of Alzheimer's Disease and Biomimetic Modeling.

Yan Li, Danni Li, Pei Zhao, Krishnaswamy Nandakumar, Liqiu Wang, Youqiang Song

Abstract readReview
In one paragraph

Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  18. New Insights into LINC00346 and its Role in Disease.Frontiers in cell and developmental biology · 2021
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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

6 authors.

Yan LiEnergy Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.ORCID 0000-0002-8044-3746
Danni LiDepartment of Neurology, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250013, China.
Pei ZhaoEnergy Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Krishnaswamy NandakumarEnergy Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Liqiu WangDepartment of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, China.ORCID 0000-0002-6514-4211
Youqiang SongSchool of Biomedical Sciences, The University of Hong Kong, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection and accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and disease treatment. Microfluidic technology is emerging as an economical and versatile platform in disease detection and diagnosis. It can be conveniently integrated with nanotechnology and/or biological models for biomedical functional and pre-clinical treatment study. These strengths make it advantageous in disease biomarker detection and functional analysis against a wide range of biological backgrounds. This review highlights the recent developments and trends of microfluidic applications in AD research. The first part looks at the principles and methods for AD diagnostic biomarker detection and profiling. The second part discusses how microfluidic chips, especially organ-on-a-chip platforms, could be used as an independent approach and/or integrated with other technologies in AD biomimetic functional analysis.

Indexed as

AD biomarkersAlzheimer’s disease (AD)blood–brain barrier (BBB)microfluidic chipsthree-dimensional AD model

Identifiers

PMID32825153
PMCPMC7569794

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.