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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

You might also read

Related Articles

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

Sort by
Same author

Recoverability-guided reduced-target inversion for microwave imaging: a synthetic breast-imaging study.

Biomedical physics & engineering express·2026
Same author

Breast Ultrasound AI Under Dataset Shift: A Patient-Leakage-Aware Benchmark.

Diagnostics (Basel, Switzerland)·2026
Same author

Viscoelastic Properties of Porcine Pericardium Under Biaxial Tensile Creep and Stress Relaxation: Application for Novel Aortic Valve Bioprosthesis Design.

Bioengineering (Basel, Switzerland)·2026
Same author

Chronic pain: The prevalence of chronic pain in patients attending Soshanguve Community Health Centre.

Canadian journal of pain = Revue canadienne de la douleur·2024
Same author

Microwave Imaging and Sensing Techniques for Breast Cancer Detection.

Micromachines·2023
Same author

Holographic Microwave Image Classification Using a Convolutional Neural Network.

Micromachines·2022

Related Experiment Video

Updated: Jul 9, 2026

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry
13:13

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry

Published on: September 23, 2014

30.8K

Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency.

Tumo Kgabeng1, Lulu Wang1,2, Harry M Ngwangwa1

  • 1Unisa Biomedical Engineering Research Group, Department of Mechanical, Bioresources, and Biomedical Engineering, School of Engineering and Built Environment, College of Science, Engineering and Technology, University of South Africa (UNISA)-Florida Science Campus, Roodepoort 1709, South Africa.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

Artificial intelligence and spatial multi-omics are revolutionizing cardiac regeneration by decoding cell dynamics in cardiomyocyte differentiation. This review synthesizes 88 studies, highlighting deep learning

Keywords:
cardiac regenerationcardiomyocyte differentiationdeep learninggraph neural networksrecurrent neural networksspatial omics

More Related Videos

Analyzing the α-Actinin Network in Human iPSC-Derived Cardiomyocytes Using Single Molecule Localization Microscopy
07:02

Analyzing the α-Actinin Network in Human iPSC-Derived Cardiomyocytes Using Single Molecule Localization Microscopy

Published on: November 3, 2020

7.1K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.1K

Related Experiment Videos

Last Updated: Jul 9, 2026

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry
13:13

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry

Published on: September 23, 2014

30.8K
Analyzing the α-Actinin Network in Human iPSC-Derived Cardiomyocytes Using Single Molecule Localization Microscopy
07:02

Analyzing the α-Actinin Network in Human iPSC-Derived Cardiomyocytes Using Single Molecule Localization Microscopy

Published on: November 3, 2020

7.1K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

1.1K

Area of Science:

  • Cardiovascular Research
  • Regenerative Medicine
  • Computational Biology

Background:

  • Cardiac regenerative medicine is rapidly advancing.
  • Understanding cardiomyocyte differentiation is crucial for cardiac repair.
  • Spatial multi-omics and AI are emerging as key technologies in this field.

Purpose of the Study:

  • To systematically review the integration of artificial intelligence (AI) with spatial multi-omics technologies in cardiac regenerative medicine.
  • To explore the application of deep learning architectures, such as Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs), in analyzing cardiac single-cell and spatial omics data.
  • To establish a foundation for AI-enabled cardiac regeneration by synthesizing methodologies and innovations from recent studies.

Main Methods:

  • Systematic literature review of 88 PRISMA-selected studies published between 2015 and 2025.
  • Analysis of deep learning implementations in spatiotemporal genomics and spatial multi-omics applications within cardiac tissues.
  • Synthesis of insights on cardiomyocyte differentiation, predictive modeling, and AI applications in precision cardiology.

Main Results:

  • Spatial omics technologies have significantly enhanced the understanding of cardiac tissue organization, revealing novel cellular communities and metabolic landscapes.
  • Deep learning models, particularly GNNs and RNNs, effectively synergize with multi-modal single-cell and spatially resolved omics datasets.
  • AI integration accelerates the mechanistic understanding and therapeutic prediction in cardiac regeneration.

Conclusions:

  • The synergy between AI and spatial multi-omics provides a powerful foundation for advancing cardiac regenerative medicine.
  • These integrated approaches are crucial for deciphering complex cellular dynamics in cardiomyocyte differentiation and cardiovascular disease.
  • This review highlights the potential to accelerate clinical translation of regenerative treatments through improved AI-driven predictive models.