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

You might also read

Related Articles

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

Sort by
Same author

Early Life Screen Exposure Characteristics and Risk of Autism Spectrum Disorder - A Multivariable Analysis in a Case-Control Study.

Indian journal of pediatrics·2026
Same author

Trapping tiny pollutants: SERS-driven strategies for microplastics and nanoplastics detection.

iScience·2025
Same author

miRNA dysregulation in Duchenne muscular dystrophy comorbidities.

World journal of experimental medicine·2025
Same author

A century of advancement in entomopathogenic nematode formulation and application technology.

Journal of invertebrate pathology·2025
Same author

The Future of Diabetes Care: Exploring the Potential of Bioartificial Pancreas and Do-It-Yourself Artificial Pancreas System Innovations.

Pathobiology : journal of immunopathology, molecular and cellular biology·2025
Same author

Sustainable synthesis of truncated Au-sensors embedded within robust snipped human nails to monitor dye adulteration in real food samples.

Analytical methods : advancing methods and applications·2025

Related Experiment Video

Updated: May 9, 2025

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

1.3K

Label-free Detection of Urine Extracellular Vesicles from Duchenne Muscular Dystrophy Patients Using Surface-Enhanced

Archana Rajavel1, Jayasree Kumar2, Narayanan Essakipillai3

  • 1Membrane Protein Interaction Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603 203, Tamil Nadu, India.

ACS Omega
|May 5, 2025
PubMed
Summary

Surface-enhanced Raman Spectroscopy (SERS) and machine learning can noninvasively detect Duchenne muscular dystrophy (DMD) in urine. This method identifies unique molecular signatures for early diagnosis and monitoring of DMD progression.

More Related Videos

Rapid Fluorescence-based Characterization of Single Extracellular Vesicles in Human Blood with Nanoparticle-tracking Analysis
09:16

Rapid Fluorescence-based Characterization of Single Extracellular Vesicles in Human Blood with Nanoparticle-tracking Analysis

Published on: January 7, 2019

9.7K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

16.9K

Related Experiment Videos

Last Updated: May 9, 2025

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

1.3K
Rapid Fluorescence-based Characterization of Single Extracellular Vesicles in Human Blood with Nanoparticle-tracking Analysis
09:16

Rapid Fluorescence-based Characterization of Single Extracellular Vesicles in Human Blood with Nanoparticle-tracking Analysis

Published on: January 7, 2019

9.7K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

16.9K

Area of Science:

  • Biochemistry
  • Nanotechnology
  • Medical Diagnostics

Background:

  • Duchenne muscular dystrophy (DMD) is a pediatric neuromuscular disease impacting males.
  • Current prognostic methods for DMD are often invasive, costly, or lack effectiveness.
  • There is a critical need for noninvasive, cost-effective tools to monitor DMD progression.

Purpose of the Study:

  • To investigate if extracellular vesicles (EVs) in urine from DMD patients show distinct biochemical profiles compared to healthy controls.
  • To evaluate the efficacy of Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning for differentiating DMD patients.
  • To establish a noninvasive diagnostic tool for early detection, staging, and monitoring of DMD.

Main Methods:

  • Urine samples were collected from 52 DMD patients and 17 healthy controls.
  • Extracellular vesicles (EVs) were isolated from urine using a Total Exosome Isolation kit.
  • SERS with silver nanoparticles was used to capture molecular fingerprints of EVs, followed by analysis with PCA, SVM, and KNN machine learning algorithms.

Main Results:

  • Distinct biochemical differences, including alterations in phenylalanine and α-helical proteins, were observed in EVs from DMD patients.
  • The integrated SERS and KNN approach achieved 60% sensitivity and 100% specificity in distinguishing DMD patients from controls.
  • Spectral data analysis revealed distinct patterns indicative of DMD, enabling potential staging.

Conclusions:

  • SERS combined with machine learning offers a noninvasive, rapid, and accurate method for diagnosing and monitoring Duchenne muscular dystrophy.
  • This approach holds significant potential for early detection and personalized treatment strategies in DMD.
  • The findings pave the way for improved patient outcomes and quality of life through advanced diagnostic capabilities.