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

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...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

You might also read

Related Articles

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

Sort by
Same author

Molecular epidemiology and emerging <i>tet</i>(X)-associated resistance of <i>Elizabethkingia</i> spp. in Taiwan, 2016-2022.

Antimicrobial agents and chemotherapy·2026
Same author

Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.

Spine deformity·2026
Same author

A combined genetic and phenotypic marker approach enables precise detection of hypervirulent <i>Klebsiella pneumoniae</i> and reveals associated traits of capsule overproduction and tellurite resistance.

Microbiology spectrum·2026
Same author

Comprehensive Genomic and Phenotypic Characterization of <i>Escherichia coli</i> O78:H9 Strain HPVN24 Isolated from Diarrheic Poultry in Vietnam.

Microorganisms·2025
Same author

Emergence of conjugative metallo-β-lactamase-encoding plasmids in the Klebsiella pneumoniae species complex: First identification of IMP-8-MCR-9.1 in K. quasipneumoniae and VIM-1 in K. variicola in Taiwan.

Journal of infection and public health·2025
Same author

ColoPola: A polarimetric imaging dataset for colorectal cancer detection.

GigaScience·2025

Related Experiment Video

Updated: Jun 3, 2026

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
08:12

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

Refractive Index Spectral Fingerprints of Pathogenic Bacteria Revealed by Monte Carlo-Optimized Opto-Microfluidic

Quoc-Thinh Dinh1, Hsin-Yu Chuang1, Dang Khoa Tong2

  • 1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Journal of Biophotonics
|June 2, 2026
PubMed
Summary

This study introduces a label-free biophotonic method using refractive index spectral signatures for rapid bacterial identification. The technique accurately differentiates bacterial species in turbid media, advancing microbial diagnostics.

Keywords:
bacterial classificationgenetic algorithmopto‐microfluidic chippathogenic bacteriarefractive index spectrum

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 3, 2026

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
08:12

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

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

Area of Science:

  • Biophotonics
  • Microbial Diagnostics
  • Spectroscopy

Background:

  • Bacterial species identification in turbid media is difficult due to light scattering and absorption.
  • Current methods often require labels or are time-consuming.

Purpose of the Study:

  • To develop a physics-informed, label-free biophotonic framework for bacterial identification.
  • To utilize refractive index (RI) spectral signatures for differentiating bacterial species.

Main Methods:

  • An opto-microfluidic chip was designed to measure transmission spectra of bacterial samples.
  • A hybrid modeling approach (Monte Carlo, Mie theory, genetic algorithm) was used to reconstruct wavelength-dependent RI spectra.
  • A convolutional neural network (CNN) classified bacteria based on RI spectral fingerprints.

Main Results:

  • Reconstructed RI spectra showed distinct interspecies differences among eight clinically relevant pathogens.
  • Higher RI values were observed for Escherichia coli and Klebsiella pneumoniae.
  • Lower RI values were observed for Staphylococcus aureus.

Conclusions:

  • Refractive index spectral fingerprints offer a promising avenue for label-free microbial identification.
  • The developed framework demonstrates potential for on-chip biophotonic diagnostics.
  • Achieved 97.75% classification accuracy highlights the method's efficacy.