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

Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.5K
Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
1.5K
Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

2.7K
The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
2.7K
Anatomy of the Heart01:27

Anatomy of the Heart

108.5K
The human heart is made up of three layers of tissue that are surrounded by the pericardium, a membrane that protects and confines the heart. The outermost layer, closest to the pericardium, is the epicardium. The pericardial cavity separates the pericardium from the epicardium. Beneath the epicardium is the myocardium, the middle layer, and the endocardium, the innermost layer. There are four chambers of the heart: the right atrium, the right ventricle, the left atrium, and the left ventricle.
108.5K
Autoregulation of Blood Flow01:17

Autoregulation of Blood Flow

2.3K
Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation....
2.3K
Regulation of Stroke Volume01:27

Regulation of Stroke Volume

3.2K
The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
3.2K
Pathophysiology of Cardiac Performance01:29

Pathophysiology of Cardiac Performance

627
Typical heart performance is influenced by heart rate, rhythm, myocardial contraction, and metabolism or blood flow. The cardiac muscle exhibits distinct electrophysiological features, including pacemaker activity and calcium channel control, which play a vital role in the heart's response to various drugs. The autonomic nervous system, comprising the sympathetic and parasympathetic branches, regulates heart rate. Sympathetic activation increases heart rate, while parasympathetic activation...
627

You might also read

Related Articles

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

Sort by
Same author

Ex situ heart perfusion: a novel model for drug validation and translation.

Frontiers in cardiovascular medicine·2026
Same author

Weight loss, exercise haemodynamics, and health status with incretin therapy for heart failure with preserved ejection fraction and obesity.

European heart journal·2026
Same author

Clinical Patterns and Appropriateness of Apixaban Dosing in Patients With Atrial Fibrillation.

JACC. Advances·2026
Same author

Publisher Correction: Five archetypes of small-scale fisheries reveal a continuum of production strategies to guide governance and policymaking.

Nature food·2026
Same author

Closing the Intensification Gap: Risk-Based Prevention in Cardio-Kidney-Metabolic Care.

Journal of the American College of Cardiology·2026
Same author

Natural and evolved membrane-associated accessory proteins differentially engage SNARE machinery for AAV egress.

Journal of virology·2026
Same journal

A Preventable Congenital Heart Malformation Syndrome Caused by a Mutation in the Glycolytic Gene PFKP.

JACC. Basic to translational science·2026
Same journal

Plasma Proteomic Signatures of Left Atrial Dysfunction and Cerebral Small Vessel Disease: Elucidating Heart-Brain Connections.

JACC. Basic to translational science·2026
Same journal

Macrophage-Specific SPP1 Contributes to Pressure Overload-Induced Cardiac Dysfunction and Maladaptive Remodeling.

JACC. Basic to translational science·2026
Same journal

Increased Arrhythmic Risk in Obesity Is Transduced by Adipose Tissue-Derived Extracellular Vesicles.

JACC. Basic to translational science·2026
Same journal

Cardiac Impulse Propagation: An Integrated View.

JACC. Basic to translational science·2026
Same journal

Recognizing Early Career Translational Investigators.

JACC. Basic to translational science·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.6K

Deep Learning Resolves Myovascular Dynamics in the Failing Human Heart.

Anish Karpurapu1, Helen A Williams1, Paige DeBenedittis1

  • 1Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.

JACC. Basic to Translational Science
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

CardioCount, a new deep learning tool, analyzes heart cell images to reveal coupled growth in adult human hearts. It also links vascular rarefaction and cell enlargement in heart failure.

Keywords:
LVADUNetscardiomyocyte cell cycleheart failurevascular rarefaction

More Related Videos

Capturing the Cardiac Injury Response of Targeted Cell Populations via Cleared Heart Three-Dimensional Imaging
08:14

Capturing the Cardiac Injury Response of Targeted Cell Populations via Cleared Heart Three-Dimensional Imaging

Published on: March 17, 2020

6.0K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.4K

Related Experiment Videos

Last Updated: Jun 21, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.6K
Capturing the Cardiac Injury Response of Targeted Cell Populations via Cleared Heart Three-Dimensional Imaging
08:14

Capturing the Cardiac Injury Response of Targeted Cell Populations via Cleared Heart Three-Dimensional Imaging

Published on: March 17, 2020

6.0K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.4K

Area of Science:

  • Cardiovascular biology
  • Computational pathology
  • Biomedical imaging analysis

Background:

  • The adult mammalian heart has limited cardiomyocyte (CM) proliferation.
  • Quantifying CM cell division requires extensive microscopic image analysis.
  • Existing methods are labor-intensive and may lack scalability.

Purpose of the Study:

  • To introduce CardioCount, a deep learning pipeline for automated nuclei scoring in microscopy images.
  • To investigate cardiomyocyte and cardiac endothelial cell interactions in the adult human heart.
  • To explore the relationship between vascular rarefaction and CM hypertrophy in heart failure.

Main Methods:

  • Development of a deep learning-based image analysis pipeline named CardioCount.
  • Application of CardioCount to a large dataset of 368,434 human microscopic cardiac images.
  • Statistical analysis to correlate cellular changes with disease states.

Main Results:

  • Evidence of coupled growth between cardiomyocytes and cardiac endothelial cells in the adult human heart.
  • Demonstration of an interrelationship between vascular rarefaction and CM hypertrophy in end-stage heart failure.
  • CardioCount provides a scalable and rigorous method for analyzing cardiac cell populations.

Conclusions:

  • CardioCount facilitates the quantitative analysis of cellular dynamics in cardiac tissue.
  • The findings highlight coordinated growth mechanisms in the healthy adult heart.
  • The study elucidates pathological interactions in heart failure, offering potential therapeutic targets.