Evidence map›Paper›PMID 37630106›Full record

ArticleMicromachines2023

Continuous Flow Separation of Live and Dead Cells Using Gravity Sedimentation.

Adem Ozcelik, Sinan Gucluer, Tugce Keskin

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2023. 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
0.5field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Dynamic and precise electromagnetic levitation of single cells.Proceedings of the National Academy of Sciences of the United States of America · 2025
    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

3 authors at 1 institution in 1 country.

Adem OzcelikDepartment of Mechanical Engineering, Aydin Adnan Menderes University, Aydin 09010, Türkiye.ORCID 0000-0002-3124-795X
Sinan GucluerDepartment of Mechanical Engineering, Aydin Adnan Menderes University, Aydin 09010, Türkiye.ORCID 0000-0001-7110-901X
Tugce KeskinDepartment of Mechanical Engineering, Aydin Adnan Menderes University, Aydin 09010, Türkiye.
Adnan Menderes University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The separation of target cell species is an important step for various biomedical applications ranging from single cell studies to drug testing and cell-based therapies. The purity of cell solutions is critical for therapeutic application. For example, dead cells and debris can negatively affect the efficacy of cell-based therapies. This study presents a cost-effective method for the continuous separation of live and dead cells using a 3D resin-printed microfluidic device. Saccharomyces cerevisiae yeast cells are used for cell separation experiments. Both numerical and experimental studies are presented to show the effectiveness of the presented device for the isolation of dead cells from cell solutions. The experimental results show that the 3D-printed microfluidic device successfully separates live and dead cells based on density differences. Separation efficiencies of over 95% are achieved at optimum flow rates, resulting in purer cell populations in the outlets. This study highlights the simplicity, cost-effectiveness, and potential applications of the 3D-printed microfluidic device for cell separation. The implementation of 3D printing technology in microfluidics holds promise for advancing the field and enabling the production of customized devices for biomedical applications.

Indexed as

continuous flow cell separationlab-on-chipmicrofluidic cell separationmicrofluidics

Identifiers

PMID37630106
PMCPMC10456911
OpenAlexW4385666386

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.