Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Development of a spatial radiomics nomogram for predicting unfavorable outcomes after acute ischemic stroke: A multicenter study.

European journal of radiology·2026
Same author

Biomechanical mechanisms of early gait training on knee cartilage degeneration after anterior cruciate ligament reconstruction: a study protocol.

Frontiers in sports and active living·2026
Same author

Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.

BMC medicine·2026
Same author

Effects of the space environment on articular cartilage homeostasis: a review.

NPJ microgravity·2026
Same author

The Relationship between Glymphatic Dysfunction and Post-stroke Cognitive Impairment.

Current medical imaging·2026
Same author

Multimodal MRI-based nomogram integrating clinical-radiological, radiomic, and habitat features to discriminate solitary fibrous tumors from atypical meningiomas.

Scientific reports·2026

Related Experiment Video

Updated: May 29, 2026

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
06:12

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

Published on: March 17, 2023

Matrix-Boosted Electrochemiluminescence Biosensor for Ultrasensitive Exosome Detection and Automated Phenotype

Yacheng Shi1,2, Yang Wu3, Zhiyong Yan4

  • 1College of Geography and Environmental Sciences, College of Chemistry and Materials Science, Zhejiang Normal University, Jinhua 321004, China.

ACS Sensors
|May 28, 2026
PubMed
Summary

This study presents an ultrasensitive electrochemiluminescence biosensor for detecting exosomes, crucial for early cancer diagnosis. The AI-powered platform accurately distinguishes exosome phenotypes in serum, advancing liquid biopsy techniques.

Keywords:
MXenesaptasensorelectrochemiluminescenceexosomeliquid biopsymachine learningphenotype discrimination

More Related Videos

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
09:30

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Published on: May 24, 2019

Related Experiment Videos

Last Updated: May 29, 2026

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
06:12

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

Published on: March 17, 2023

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
09:30

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Published on: May 24, 2019

Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Cancer Diagnostics

Background:

  • Accurate exosome quantification and phenotypic discrimination in biological fluids are critical for early cancer diagnosis but remain challenging.
  • Existing methods often lack the sensitivity and specificity required for complex clinical samples.

Purpose of the Study:

  • To develop an ultrasensitive electrochemiluminescence (ECL) biosensor for accurate exosome detection and phenotypic discrimination.
  • To establish an AI-powered platform for automated exosome analysis in liquid biopsies.

Main Methods:

  • Engineered an ECL biosensor using Cp-Pt-Ti3-xC2Ty MXene emitters and a proximity-dependent aptamer strategy.
  • Utilized a "signal-off-on" mechanism triggered by target exosome recognition and methylene blue-labeled aptamer desorption.
  • Applied a support vector machine (SVM) algorithm and 3D principal component analysis for data processing and phenotype discrimination.

Main Results:

  • Achieved high quenching efficiency (99.8%) and a low limit of detection (35 particles/μL) for MCF-7 exosomes.
  • Demonstrated highly accurate and automated discrimination of exosome phenotypes from distinct cell lines in clinical serum samples.
  • Validated the biosensor's performance without the need for enzymatic amplification.

Conclusions:

  • The developed ECL biosensor offers a sensitive and specific platform for exosome analysis in liquid biopsies.
  • The integration of AI algorithms enables automated and accurate discrimination of cancer-related exosome phenotypes.
  • This AI-empowered approach represents a significant advancement in early cancer diagnosis and personalized medicine.