Evidence map›Paper›PMID 32577148›Full record

ArticleBiomicrofluidics2020

Isolation of cancer-derived extracellular vesicle subpopulations by a size-selective microfluidic platform.

Zheyuan Chen, Yi Yang, Hirohito Yamaguchi, Mien-Chi Hung, Jun Kameoka

Abstract read
In one paragraph

Article in Biomicrofluidics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  7. Isolation and characterization of extracellular vesicles and future directions in diagnosis and therapy.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2023
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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.

Zheyuan ChenDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77840, USA.
Yi YangDepartment of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Hirohito YamaguchiCancer Research Center, Qatar Biomedical Research Institute, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
Mien-Chi Hung

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) play an important role in intercellular communication. Recently, there has been increasing interest in EVs as potential diagnostic biomarkers and therapeutic vehicles. However, the molecular properties and cargo information of EV subpopulations have not yet been fully investigated due to lack of reliable and reproducible EV separation technology. Current approaches have faced difficulties with efficiently isolating EVs from biofluids, especially subpopulations of small EVs. Here, we report an EV isolation method based on a size-selective microfluidic platform (ExoSMP) via nanomembrane filtration and electrophoretic force. This unique platform offers an enhanced approach to sorting a heterogeneous population of EVs based on size, with the additional advantages of being label-free and low-cost, and featuring a short processing time (<1 h), and convenient integration with downstream analysis. In this research, we used ExoSMP to demonstrate the isolation of cancer-derived small EVs (30-120 nm) with high recovery (94.2%) and reproducibility at an optimum sample flow rate. Furthermore, we investigated isolation of EV subpopulations by altering nanomembrane combinations with different pore size combinations (50 and 100 nm, 30 and 100 nm, 30 and 200 nm, and 30 and 50 nm). This ExoSMP technique can serve as a standardized EV isolation/separation tool, facilitating the clinical prospects of EVs and opening up a new avenue for future point-of-care applications in liquid biopsies.

Identifiers

PMID32577148
PMCPMC7307647

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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.