Evidence map›Paper›PMID 41360752›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Anionic Liposomes as Optimal Membrane Fusion Carriers Enabling in Situ Multiplexed Detection of Extracellular Vesicle MicroRNAs.

Jing-Yuan Ma, Xiao Wang, Yuhan Cai, Guancheng Wang, Mingze Lu, Kaizheng Feng, Ying Zhao, Xue Wu, Xiaoping Zhang, Haoan Wu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

14 authors.

Jing-Yuan MaState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.ORCID https://orcid.org/0000-0002-9163-4441
Xiao WangState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Yuhan CaiState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Guancheng WangState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Mingze LuState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Kaizheng FengState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Ying ZhaoState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Xue WuDepartment of Hematology, Zhongda Hospital, School of Medicine, Institute of Hematology, Zhongda Hospital Southeast University, Nanjing, Jiangsu, 210009, P. R. China.
Xiaoping ZhangDepartment of Hematology, Zhongda Hospital, School of Medicine, Institute of Hematology, Zhongda Hospital Southeast University, Nanjing, Jiangsu, 210009, P. R. China.
Haoan WuState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Wei YuDepartment of Medical Laboratory, Taikang Xianlin Drum Tower Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210000, P. R. China.
Ming MaState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.
Zheng GeDepartment of Hematology, Zhongda Hospital, School of Medicine, Institute of Hematology, Zhongda Hospital Southeast University, Nanjing, Jiangsu, 210009, P. R. China.
Yu ZhangState Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, P. R. China.ORCID https://orcid.org/0000-0002-0228-7979

Funding

Frontier Technologies R&D Program of Jiangsu BF2024062National Key Research and Development Program of China 2022YFA1205802National Key Research and Development Program of China 2022YFC2406504National Natural Science Foundation of China 82302370National Natural Science Foundation of China 82572399Natural Science Foundation of Jiangsu Province BK20230836Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX24_0471SEU Innovation Capability Enhancement Plan for Doctoral Students CXJH_SEU 25140
6 · The paper itself

Abstract

Extracellular vesicle (EV) microRNAs (miRNAs) are promising liquid biopsy biomarkers for non-invasive diagnosis, monitoring, and therapeutic evaluation of cancer. However, sensitive EV miRNA detection is hindered by complex pre-analytical processing. Here, the authors present an anionic liposome (AL) assisted membrane fusion strategy enabling one-step multiplexed quantification of EV miRNAs directly from plasma without EV isolation or RNA extraction, termed EValarm (Anionic Liposome Assisted miRNAs Monitoring for Extracellular Vesicles). Liposomes encapsulating probes are prepared using a microfluidic chip, achieving catalytic signal amplification after target recognition of miRNA. Systematic lipid screening identified ALs as optimal carriers, exhibiting minimal background and superior sensitivity compared to cationic and neutral liposomes. The AL-based assay delivered accuracy comparable to quantitative PCR with a streamlined workflow. Applied to 106 clinical samples from lymphoma patients and healthy controls, integration with artificial intelligence achieved high accuracy (AUC > 0.99). In summary, this study demonstrates a platform enabling direct and sensitive plasma EV miRNA detection, offering strong potential for clinical translation in cancer liquid biopsy.

Indexed as

Extracellular VesiclesLiposomesMembrane FusionMicroRNAsAnionsBiomarkers, TumorHumansLiquid BiopsyAnionsBiomarkers, TumorLiposomesMicroRNAsextracellular vesiclesliposomesliquid biopsymembrane fusionmicroRNA detection

Identifiers

PMID41360752
PMCPMC12915101

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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.