Evidence map›Paper›PMID 28282412›Full record

ArticlePloS one2017

Bead mediated separation of microparticles in droplets.

Sida Wang, Ki-Joo Sung, Xiaoxia Nina Lin, Mark A Burns

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

4 authors.

Sida WangDepartment of Chemical Engineering, University of Michigan-Ann Arbor, Ann Arbor, MI, United States of America.ORCID http://orcid.org/0000-0001-8101-8883
Ki-Joo SungDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
Xiaoxia Nina LinDepartment of Chemical Engineering, University of Michigan-Ann Arbor, Ann Arbor, MI, United States of America.
Mark A BurnsDepartment of Chemical Engineering, University of Michigan-Ann Arbor, Ann Arbor, MI, United States of America.

Funding

Cellular Biotechnology Training Program (CBTP)T32GM008353 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SCHWENDEMAN, ANNA · 1991 to 2021
$9.3M
Cellular Biotechnology Training Program (CBTP) - Years 31-35T32GM145304 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Guizhi Zhu · 2022 to 2026
$2.6M
NIGMS NIH HHS T32 GM008353NIGMS NIH HHS T32 GM145304
6 · The paper itself

Abstract

Exchange of components such as particles and cells in droplets is important and highly desired in droplet microfluidic assays, and many current technologies use electrical or magnetic fields to accomplish this process. Bead-based microfluidic techniques offer an alternative approach that uses the bead's solid surface to immobilize targets like particles or biological material. In this paper, we demonstrate a bead-based technique for exchanging droplet content by separating fluorescent microparticles in a microfluidic device. The device uses posts to filter surface-functionalized beads from a droplet and re-capture the filtered beads in a new droplet. With post spacing of 7 μm, beads above 10 μm had 100% capture efficiency. We demonstrate the efficacy of this system using targeted particles that bind onto the functionalized beads and are, therefore, transferred from one solution to another in the device. Binding capacity tests performed in the bulk phase showed an average binding capacity of 5 particles to each bead. The microfluidic device successfully separated the targeted particles from the non-targeted particles with up to 98% purity and 100% yield.

Indexed as

FluorescenceMicrofluidic Analytical Techniques

Identifiers

PMID28282412
PMCPMC5345812

What OpenQuestion holds

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LicenceCC BY
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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.