Evidence map›Paper›PMID 42041441›Full record

ArticleBiosensors2026

Efficient On-Chip Separation and Labeling of Extracellular Vesicles from Whole Blood.

Jian Feng, Zhichen Li, Haoyang Shen, Rui Hao, Yifei Yang, Xi Chen, Xin Hong, Guoqiang Gu, Lin Zeng, Hui Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2026. 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
–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

0 citing papers in PubMed.

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

10 authors.

Jian FengDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Zhichen LiDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Haoyang ShenDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Rui HaoLaboratory of Biomedical Microsystems and Nano Devices, Center for Bionic Sensing and Intelligence, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.ORCID 0000-0002-0655-4122
Yifei YangDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Xi ChenLaboratory of Biomedical Microsystems and Nano Devices, Center for Bionic Sensing and Intelligence, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Xin HongDepartment of Biochemistry, SUSTech Homeostatic Medicine Institute, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.ORCID 0000-0002-9671-0116
Guoqiang GuLaboratory of Biomedical Microsystems and Nano Devices, Center for Bionic Sensing and Intelligence, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Lin ZengLaboratory of Biomedical Microsystems and Nano Devices, Center for Bionic Sensing and Intelligence, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Hui YangSchool of Biomedical Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China.

Funding

China Postdoctoral Science Foundation 2024M763449Ministry of Science and Technology of China 2023YFF0721500National Natural Science Foundation of China 52405634, 62475279, 52505649
6 · The paper itself

Abstract

The development of high-throughput technologies for the separation and labeling of extracellular vesicles (EVs) from whole blood is critical for downstream EV detection and analysis. However, conventional EV separation and labeling workflows are typically labor-intensive and inefficient, requiring multiple sequential processing steps. Here, we present a microfluidic platform that integrates negative magnetophoresis-based separation with mixing-enhanced on-chip labeling. The chip adopts a vertical flow channel architecture in combination with a Halbach-array magnetic field configuration, thereby overcoming the throughput limitations inherent to traditional horizontal microchannels. Parallel channels can be freely arranged above on the magnetic array to achieve ultra-high throughput processing, achieving a cell removal efficiency of 99.97% at a blood-to-sheath flow ratio of 1:5. Furthermore, by incorporating a narrow-wide channel design synergized with a herringbone-Tesla micromixer structure, the platform achieves a labeling efficiency of 91.8% within 2 min, approaching the performance of conventional 20 min incubation. This system offers both high-throughput and integration capabilities, providing a powerful technical platform for EV-related life science research.

Indexed as

Extracellular VesiclesHumansLab-On-A-Chip DevicesMicrofluidic Analytical Techniquesextracellular vesicleshigh-throughput separationnegative magnetophoresison-chip labelingtesla micromixer

Identifiers

PMID42041441
PMCPMC13113986

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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.