Evidence map›Paper›PMID 36896355›Full record

ArticleBiomicrofluidics2023

On-chip dielectrophoretic device for cancer cell manipulation: A numerical and artificial neural network study.

Rasool Mohammadi, Hadi Afsaneh, Behnam Rezaei, Mahdi Moghimi Zand

Open access · greenAbstract read
In one paragraph

Article in Biomicrofluidics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 40% 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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 2 countries.

Rasool MohammadiSchool of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 11155-463, Iran.ORCID 0000-0002-3005-2309
Hadi AfsanehDepartment of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.ORCID 0000-0001-5500-7734
Behnam RezaeiSmall Medical Devices, BioMEMS, and LoC Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 11155-463, Iran.ORCID 0000-0001-9446-2473
Mahdi Moghimi ZandSmall Medical Devices, BioMEMS, and LoC Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 11155-463, Iran.ORCID 0000-0002-7868-1438
University of Tehran · IRUniversity of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer, as one of the most frequent types of cancer in women, imposes large financial and human losses annually. MCF-7, a well-known cell line isolated from the breast tissue of cancer patients, is usually used in breast cancer research. Microfluidics is a newly established technique that provides many benefits, such as sample volume reduction, high-resolution operations, and multiple parallel analyses for various cell studies. This numerical study presents a novel microfluidic chip for the separation of MCF-7 cells from other blood cells, considering the effect of dielectrophoretic force. An artificial neural network, a novel tool for pattern recognition and data prediction, is implemented in this research. To prevent hyperthermia in cells, the temperature should not exceed 35 °C. In the first part, the effect of flow rate and applied voltage on the separation time, focusing efficiency, and maximum temperature of the field is investigated. The results denote that the separation time is affected by both the input parameters inversely, whereas the two remaining parameters increase with the input voltage and decrease with the sheath flow rate. A maximum focusing efficiency of 81% is achieved with a purity of 100% for a flow rate of

Identifiers

PMID36896355
PMCPMC9991445
OpenAlexW4323309742

What OpenQuestion holds

Textmetadata
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.