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

LLPS-based classification and a novel prognostic signature reveal NRF1 as a therapeutic target in pancreatic cancer.

Cancer cell international·2026
Same author

Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

808 nm laser diode with a stepped-structure heat sink for high-efficiency laser wireless power transmission.

Optics letters·2026
Same author

Uniformly modified iron nanoclusters synergistically enhance Interface stability and electrochemical performance of silicon anodes.

Journal of colloid and interface science·2026
Same author

Circ_0075691 regulates lipid metabolism in granulosa cells by interacting with EIF4A3 to promote PTGS2 mRNA stability.

Biochimica et biophysica acta. Molecular basis of disease·2026
Same author

Sonogenic malate depleting modulator for tumor metabolic reprogramming and antitumor immune activation.

Bioactive materials·2025
Same journal

Strain-Level Food Surveillance of <i>Escherichia coli</i> Using a Specific-Nonspecific Hybrid Sensor Array Strategy.

Analytical chemistry·2026
Same journal

A Field-Portable Fe(IV)-Mediated Competitive Quenching Chemiluminescence Platform with a Synchronous Y-Shaped Flow-through Cell for Broad-Spectrum Quantification of Volatile Phenols.

Analytical chemistry·2026
Same journal

Single-Molecule Characterization of CRISPR-Cas12a for Amplification-Free Genetic Testing.

Analytical chemistry·2026
Same journal

Integrated Acoustofluidic Manipulation and Oscillation-Stabilized Magnetic Relaxation Biosensing for <i>Salmonella</i> Detection.

Analytical chemistry·2026
Same journal

A Self-Powered Sensing Platform Based on the Janus Heterostructure for Machine Learning-Assisted Dual-Mode Detection of 17β-Estradiol.

Analytical chemistry·2026
Same journal

Large Language Model-Generated Dietary Metabolite Biomarker Database Drives Deep Annotation of the Human Diet Metabolome.

Analytical chemistry·2026
See all related articles

Related Experiment Video

Updated: May 28, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.0K

Single-Cell Array Enhanced Cell Damage Recognition Using Artificial Intelligence for Anticancer Drug Discovery.

Qingxuan Li1, Jiangshan Zhou1, Songyao Jiang2

  • 1Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States.

Analytical Chemistry
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered method using single-cell arrays to accurately recognize cancer cell damage. The approach efficiently assesses drug toxicity and aids in screening potential anticancer drugs.

More Related Videos

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.4K
Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties
08:19

Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties

Published on: May 19, 2023

1.6K

Related Experiment Videos

Last Updated: May 28, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.0K
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.4K
Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties
08:19

Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties

Published on: May 19, 2023

1.6K

Area of Science:

  • Biotechnology
  • Artificial Intelligence
  • Cancer Research

Background:

  • Accurate assessment of drug-induced cell damage is crucial for cancer therapy and drug development.
  • Traditional methods often face challenges with cell segmentation, background noise, and computational efficiency.
  • Developing high-throughput, accurate methods for monitoring cell health is essential for personalized medicine.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based method for recognizing cell damage using single-cell arrays.
  • To improve the accuracy and efficiency of toxicity assessment for potential anticancer drugs.
  • To enable precise classification of cancer cell status and damage levels.

Main Methods:

  • Utilized single-cell micropatterns (micropatches and microwells) to isolate individual cells, minimizing overlap and preserving cell contours.
  • Employed fluorescence microscopy to monitor morphology and reactive oxygen species intensity changes in cells exposed to therapeutic drugs like doxorubicin.
  • Trained a convolutional neural network (CNN) model using time-series images of cancer cells before and after drug exposure at varying concentrations.

Main Results:

  • The AI model accurately identified cancer cell status (live/dead) and classified damage levels (major/moderate/minor).
  • The single-cell array approach eliminated the need for computational cell segmentation, reducing background noise and interference.
  • Enhanced accuracy in image recognition and accelerated computational analysis for toxicity prediction.

Conclusions:

  • The developed single-cell array method combined with AI offers a highly accurate and efficient platform for cell damage recognition.
  • This AI-driven approach accelerates toxicity analysis, facilitating the screening of potential anticancer drugs.
  • The method holds promise for advancing cancer research and drug discovery pipelines.