Evidence map›Paper›PMID 40985328›Full record

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

Deep Learning-Powered Nanoplasmonic Biosensing Approach Enables Ultrasensitive Extracellular Vesicles Profiling for Cancer Screening.

Jiaheng Zhu, Yingqi Xiao, Xinyue Huang, Qiang Niu, Lihuang Zeng, Shaowei Lin, Mengqi Jiang, Tianhao Huang, Hanyang Chen, Yinong Xie and 12 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Strategies for Multiplexing Plasmonic Biosensing.Sensors (Basel, Switzerland) · 2026
    Review
  2. Article
  3. Review
  4. 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

22 authors.

Jiaheng ZhuInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Yingqi XiaoDepartment of Laboratory Medicine, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.
Xinyue HuangInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Qiang NiuInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Lihuang ZengInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Shaowei LinThe First Affiliated Hospital of Xiamen University School of Medicine, Xiamen University, Xiamen, 361003, China.
Mengqi JiangInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Tianhao HuangInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Hanyang ChenInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Yinong XieDepartment of Automation, Tsinghua University, Beijing, 100084, China.
Yuan GaoInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Wei ChenInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Yiming YanInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Jiaqing ShenInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Kaibin ChenInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Yurong DaiInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Zhipeng ZhangInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Lijun ZengDepartment of Laboratory Medicine, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.
Yahong ChenInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Boan LiDepartment of Laboratory Medicine, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.
Jinfeng ZhuInstitute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China.
Bo LiDepartment of Laboratory Medicine, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100039, China.ORCID https://orcid.org/0009-0004-2341-1459

Funding

Capital's Funds for Health Improvement and Research SF2024-1-2171NSAF U2130112NSFC 62175205Shenzhen Science and Technology Development Funds JCYJ20220530143015035Youth Talent Support Program of Fujian Province
6 · The paper itself

Abstract

Nanoplasmonic metasurface technology, known for its high sensitivity, has garnered significant attention in the field of cancer detection. However, its potential is currently hindered by the inefficient data processing and analysis of conventional biosensing approaches. Herein, a biosensing strategy based on the Kolmogorov-Arnold network (KAN)-enabled metasurface chip (metaEVchip) for ultrasensitive small extracellular vesicles (sEV) analysis in serum is proposed. By analyzing full-spectrum data from 600 pancreatic ductal adenocarcinoma (PDAC) patients and 1200 controls via KAN-powered deep learning nanoplasmonic biosensing, the strategy achieves an exceptional area under the curve (AUC) of 0.99 in an external validation set, outperforming traditional methods. Further exploration of this enhanced performance reveals KAN's mechanism for the simultaneous capture of multi-dimensional spectral features, an advantage that enables efficient data processing and accuracy. This advancement significantly expands the applicability of nanoplasmonic metasurfaces in biosensing and establishes a new paradigm for cancer screening and improved clinical management of multiple malignancies.

Indexed as

Biosensing TechniquesCarcinoma, Pancreatic DuctalDeep LearningEarly Detection of CancerExtracellular VesiclesPancreatic NeoplasmsHumanscancer screeningdeep learningextracellular vesicleKolmogorov‐Arnold networkmetasurfacenanoplasmonic

Identifiers

PMID40985328
PMCPMC12786282

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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.