Jove
Visualize
Contact Us

Related Concept Videos

Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

You might also read

Related Articles

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

Sort by
Same author

Multi-Sequence Guided Generation of Contrast-Enhanced Magnetic Resonance Imaging Using Diffusion Models.

Bioengineering (Basel, Switzerland)·2026
Same author

Elastic scattering spectrum fused with Raman spectrum for rapid classification of colorectal cancer tissues.

Analytical methods : advancing methods and applications·2025
Same author

Recent advances in microfluidic-based spectroscopic approaches for pathogen detection.

Biomicrofluidics·2024
Same author

Efficient Pause Extraction and Encode Strategy for Alzheimer's Disease Detection Using Only Acoustic Features from Spontaneous Speech.

Brain sciences·2023
Same author

DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer.

Entropy (Basel, Switzerland)·2023
Same author

Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria.

Biosensors·2022
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 Experiment Video

Updated: Jul 2, 2026

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres
11:09

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres

Published on: October 23, 2011

BiFusionPathoNet: fusion network for drug-resistant bacteria identification via optical scattering patterns.

Yichuan Wang1, Xu He2, Mubashir Hussain1,3

  • 1Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China. liubin@njmu.edu.cn.

Analytical Methods : Advancing Methods and Applications
|January 13, 2025
PubMed
Summary

This study introduces an AI-powered method using light and Raman scattering to rapidly detect drug-resistant bacteria like methicillin-resistant Staphylococcus aureus (MRSA). The multimodal approach achieved high accuracy, outperforming single-signal methods.

More Related Videos

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes
08:23

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

Published on: March 2, 2020

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)
04:07

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jul 2, 2026

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres
11:09

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres

Published on: October 23, 2011

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes
08:23

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

Published on: March 2, 2020

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)
04:07

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)

Published on: February 23, 2024

Area of Science:

  • Biotechnology
  • Medical Diagnostics
  • Artificial Intelligence

Background:

  • Antibiotic resistance is a growing global health threat, necessitating rapid diagnostic tools.
  • Distinguishing between methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA) is crucial for effective treatment.

Purpose of the Study:

  • To develop and evaluate a rapid, accurate method for identifying drug-resistant bacteria, specifically MRSA.
  • To compare the performance of single-modal (MDLS or Raman) and multimodal AI-driven detection models.

Main Methods:

  • A microfluidic platform integrated with optical fibers was used to collect Multi-angle Dynamic Light Scattering (MDLS) and Raman scattering signals from bacteria.
  • Three artificial intelligence models were developed: ResistNet (MDLS), SERB-CNN (Raman), and BiFusionPathoNet (multimodal fusion).

Main Results:

  • ResistNet achieved 83.8% accuracy on MDLS data.
  • SERB-CNN attained 91.84% accuracy on public Raman data and 93.5% on custom data.
  • BiFusionPathoNet, the multimodal model, reached a superior accuracy of 96.8%, significantly outperforming single-modal approaches.

Conclusions:

  • The multimodal strategy combining MDLS and Raman scattering signals with AI is highly effective for rapid and accurate detection of drug-resistant bacteria.
  • This approach offers a promising solution for timely diagnosis and management of infections caused by resistant pathogens like MRSA.