Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

Modern Molecular Taxonomy

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

Automated Microbial Diagnostics

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

Methods to Assess Microbial Populations

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a...

You might also read

Related Articles

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

Sort by

Same author

Frequency Stability of Graphene Nonlinear Parametric Oscillator.

Nano letters·2026

Same author

Probing Viscoelasticity of Polymeric Coatings Using Nonlinear Dynamic Atomic Force Microscopy.

Small methods·2025

Same author

Hidden Vibrational Bistability Revealed by Intrinsic Fluctuations of a Carbon Nanotube.

Nano letters·2025

Same author

Nonlinear dynamics and magneto-elasticity of nanodrums near the phase transition.

Nature communications·2025

Same author

Deciphering the largest disease-associated transcript isoforms in the human neural retina with advanced long-read sequencing approaches.

Genome research·2025

Same author

Finite element-based nonlinear dynamic optimization of nanomechanical resonators.

Microsystems & nanoengineering·2025

Same journal

Plant-Plant Communication for Systemic Acquired Resistance under Biotic Stress Spatiotemporally Tracked by an <i>In Situ</i> Surface-Enhanced Raman Spectroscopy Aerosol Spraying Analyzer.

ACS sensors·2026

Same journal

Modulating Electronic Structure via Bimetallic D<i>-</i>Band Engineering toward an Ultrasensitive Sensor Platform for Caffeic Acid in Food.

ACS sensors·2026

Same journal

Indiscriminate <i>T</i><i>rans</i>-Cleavage Activity of CRISPR/SuCas12a2 Enables Sensitive Detection of SARS-CoV-2.

ACS sensors·2026

Same journal

Spin-State Engineering in 2D Metal-Organic Frameworks for Ultrasensitive Room-Temperature Ammonia Sensing.

ACS sensors·2026

Same journal

A Wearable Microneedle-Based Electrochemical Aptamer Sensor: Enabling Real-Time Dynamic NT-proBNP Monitoring for Enhanced Heart Failure Management.

ACS sensors·2026

Same journal

Double-Strand Gated Biosensor for Ultrasensitive T4 PNK Detection via λ-Exonuclease-Driven Background Suppression and Dimer G-Triplex Signal Amplification.

ACS sensors·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 19, 2026

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

Single-Cell Nanomotion and Machine Learning for Parallel Bacterial Identification and Antibiotic Screening.

Santiago Mendoza-Silva¹, Farbod Alijani¹, Le-Vaughn Naarden²

¹Department of Precision and Microsystem Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands.

|March 17, 2026

Summary

This summary is machine-generated.

This study introduces a novel diagnostic method using graphene drums and machine learning to rapidly identify bacterial species and their antibiotic resistance. This integrated approach offers accurate, label-free bacterial diagnostics within hours.

Keywords:

antibiotic susceptibility testing bacterial identification graphene biosensors machine learning nanomotion detection

More Related Videos

Single-cell Microfluidic Analysis of Bacillus subtilis

Single-cell Microfluidic Analysis of Bacillus subtilis

Published on: January 26, 2018

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Published on: December 6, 2013

Related Experiment Videos

Last Updated: Mar 19, 2026

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

Single-cell Microfluidic Analysis of Bacillus subtilis

Single-cell Microfluidic Analysis of Bacillus subtilis

Published on: January 26, 2018

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

Published on: December 6, 2013

Area of Science:

Biotechnology
Nanotechnology
Machine Learning

Background:

Accurate bacterial identification and antibiotic susceptibility testing (AST) are crucial for clinical decisions and combating antimicrobial resistance.
Current methods like MALDI-TOF and standard AST are often segmented, time-consuming, and lack concurrent identification capabilities.
Existing diagnostic tools present limitations in speed and integrated functionality for comprehensive bacterial profiling.

Purpose of the Study:

To develop a single, rapid, and accurate method for simultaneous bacterial identification and antibiotic susceptibility profiling.
To overcome the limitations of segmented diagnostic approaches by integrating nanomotion detection with machine learning.
To provide a label-free, single-cell level diagnostic tool for bacterial infections.

Main Methods:

Integration of single-cell nanomotion detection using graphene drums with machine learning (ML) algorithms.
Real-time recording of nanomotion signals (nanoscale vibrations) from single living bacterial cells.
Transformation of nanomotion signals into time-frequency spectrograms for ML model input and pattern recognition.

Main Results:

Successful differentiation of bacterial species including Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae.
Simultaneous distinction between resistant and susceptible bacterial strains with 98% precision.
Demonstration of label-free bacterial diagnostics with identification and susceptibility profiling within hours at the single-cell level.

Conclusions:

The developed framework offers a significant advancement in bacterial diagnostics by combining sensitive graphene nanomotion sensors with ML.
This integrated approach provides rapid, accurate, and simultaneous identification and antibiotic susceptibility profiling.
The technology holds promise for improving clinical decision-making and addressing the challenge of antimicrobial resistance.