Evidence map›Paper›PMID 30733497›Full record

ArticleScientific reports2019

Simultaneous acoustic and photoacoustic microfluidic flow cytometry for label-free analysis.

Vaskar Gnyawali, Eric M Strohm, Jun-Zhi Wang, Scott S H Tsai, Michael C Kolios

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

16 citing papers in PubMed, 45 citations in OpenAlex.

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  13. Cell Separations and Sorting.Analytical chemistry · 2020
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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

5 authors at 2 institutions in 1 country.

Vaskar GnyawaliDepartment of Mechanical and Industrial Engineering, Ryerson University, Toronto, Canada.ORCID 0000-0001-6083-6924
Eric M StrohmDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.
Jun-Zhi WangInstitute for Biomedical Engineering, Science and Technology (iBEST), Toronto, Canada.
Scott S H TsaiDepartment of Mechanical and Industrial Engineering, Ryerson University, Toronto, Canada.
Michael C KoliosDepartment of Physics, Ryerson University, Toronto, Canada. mkolios@ryerson.ca.ORCID 0000-0002-9994-8293
St. Michael's Hospital · CAUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We developed a label-free microfluidic acoustic flow cytometer (AFC) based on interleaved detection of ultrasound backscatter and photoacoustic waves from individual cells and particles flowing through a microfluidic channel. The AFC uses ultra-high frequency ultrasound, which has a center frequency of 375 MHz, corresponding to a wavelength of 4 μm, and a nanosecondpulsed laser, to detect individual cells. We validate the AFC by using it to count different color polystyrene microparticles and comparing the results to data from fluorescence-activated cell sorting (FACS). We also identify and count red and white blood cells in a blood sample using the AFC, and observe an excellent agreement with results obtained from FACS. This new label-free, non-destructive technique enables rapid and multi-parametric studies of individual cells of a large heterogeneous population using parameters such as ultrasound backscatter, optical absorption, and physical properties, for cell counting and sizing in biomedical and diagnostics applications.

Indexed as

AcousticsLightMicrofluidic Analytical TechniquesBlood CellsEquipment DesignFlow CytometryHumansLasersMicrofluidicsUltrasonic Waves

Identifiers

PMID30733497
PMCPMC6367457
OpenAlexW2920485059

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.