Evidence map›Paper›PMID 35208333›Full record

ArticleMicromachines2022

Multi-Modal Microfluidics (M

Yaoping Liu, Wei Wang

Abstract read
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Yaoping LiuSchool of Integrated Circuits, Peking University, Beijing 100871, China.ORCID 0000-0002-1194-0976
Wei WangSchool of Integrated Circuits, Peking University, Beijing 100871, China.

Funding

China Postdoctoral Science Foundation 2018M631261China Postdoctoral Science Foundation 2019T120018National Key R & D Program of China 2020YFC2005405
6 · The paper itself

Abstract

Liquid biopsy, the technique used to shed light on diseases via liquid samples, has displayed various advantages, including minimal invasiveness, low risk, and ease of multiple sampling for dynamic monitoring, and has drawn extensive attention from multidisciplinary fields in the past decade. With the rapid development of microfluidics, it has been possible to manipulate targets of interest including cells, microorganisms, and exosomes at a single number level, which dramatically promotes the characterization and analysis of disease-related markers, and thus improves the capability of liquid biopsy. However, when lab-ready techniques transfer into hospital-applicable tools, they still face a big challenge in processing raw clinical specimens, which are usually of a large volume and consist of rare targets drowned in complex backgrounds. Efforts toward the sample preparation of clinical specimens (i.e., recovering/concentrating the rare targets among complex backgrounds from large-volume liquids) are required to bridge the gap between the proof-of-concept demonstrations and practical applications. The throughput, sensitivity, and purity (TSP performance criteria) in sample preparation, i.e., the volume speed in processing liquid samples and the efficiencies of recovering rare targets and depleting the backgrounds, are three key factors requiring careful consideration when implementing microfluidic-based liquid biopsy for clinical practices. Platforms based on a single microfluidic module (single-modal microfluidics) can hardly fulfill all the aforementioned TSP performance criteria in clinical practices, which puts forward an urgent need to combine/couple multiple microfluidic modules into one working system (i.e., multi-modal microfluidics, M

Indexed as

liquid biopsymicrofluidicssample preparation

Identifiers

PMID35208333
PMCPMC8874502

What OpenQuestion holds

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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.