Evidence map›Paper›PMID 20179819›Full record

ReviewChemical Society reviews2010

Microfluidic stochastic confinement enhances analysis of rare cells by isolating cells and creating high density environments for control of diffusible signals.

Meghan E Vincent, Weishan Liu, Elizabeth B Haney, Rustem F Ismagilov

Abstract readReview
In one paragraph

Review in Chemical Society reviews, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Article
  2. Review
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  8. Droplet Microfluidics for Microbial Biotechnology.Advances in biochemical engineering/biotechnology · 2022
    Article
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  16. Review
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  19. [Preliminary study on the construction of three-dimensional hippocampal neural network by using microfluidic technologyZhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2019
    Article
  20. Review
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.

Meghan E VincentDepartment of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA.
Weishan Liu
Elizabeth B Haney
Rustem F Ismagilov

Funding

2007 NIH Director's Pioneer Award Program (DP1)DP1OD003584 · OD · UNIVERSITY OF CHICAGO · PI ISMAGILOV, RUSTEM F · 2007 to 2011
$3.9M
NIH HHS DP1 OD003584
6 · The paper itself

Abstract

Rare cells can be difficult to analyze because they either occur in low numbers or coexist with a more abundant cell type, yet their detection is crucial for diagnosing disease and maintaining human health. In this tutorial review, we introduce the concept of microfluidic stochastic confinement for use in detection and analysis of rare cells. Stochastic confinement provides two advantages: (1) it separates rare single cells from the bulk mixture and (2) it allows signals to locally accumulate to a higher concentration around a single cell than in the bulk mixture. Microfluidics is an attractive method for implementing stochastic confinement because it provides simple handling of small volumes. We present technologies for microfluidic stochastic confinement that utilize both wells and droplets for the detection and analysis of single cells. We address how these microfluidic technologies have been used to observe new behavior, increase speed of detection, and enhance cultivation of rare cells. We discuss potential applications of microfluidic stochastic confinement to fields such as human diagnostics and environmental testing.

Indexed as

Clinical Laboratory TechniquesCell Culture TechniquesHumansMicrofluidicsStochastic Processes

Identifiers

PMID20179819
PMCPMC2829723

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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.