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

Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

598
A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is...
598
Nonlinear Pharmacokinetics: Drug Elimination for IV Bolus Injection00:59

Nonlinear Pharmacokinetics: Drug Elimination for IV Bolus Injection

144
In pharmacokinetics, the elimination rate of a drug following a capacity-limited model is primarily controlled by two parameters: Vmax and KM. These parameters are crucial in how the drug behaves inside the body after administration.
Following the administration of a single intravenous (IV) bolus injection, we can determine the concentration of the drug in the plasma at any given time. This calculation is achieved using a specific equation that integrates the values of Vmax and KM.
We can also...
144

You might also read

Related Articles

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

Sort by
Same author

Histone H3 lysine 18 lactylation-mediated SULF1 transcription promotes atherosclerosis by regulating endothelial-to-mesenchymal transition.

Cardiovascular research·2026
Same author

Global Burden, Attributable Risk Factors, and Future Projections of Lower Extremity Peripheral Arterial Disease Among Postmenopausal Women.

International journal of women's health·2026
Same author

Making waves: Towards sustainability in wastewater management through the biological-ecological coupling system.

Water research·2026
Same author

Quantitative determination of embolization endpoints based on local arterial pressure.

Bioengineering & translational medicine·2026
Same author

Numerical Simulations of In-Plane and Transmural Tear Propagations in Aortic Dissection: Possible Mechanisms Behind Dissection Progression.

Journal of biomechanical engineering·2025
Same author

Influences of structural helicity of aortic dissection on endovascular repair.

iScience·2025

Related Experiment Video

Updated: Sep 5, 2025

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

8.5K

[Research on injection flow velocity planning method for embolic agent injection system].

Jiasheng Li1,2,3, Dongcheng Ren1,2,3, Bo Zhou4

  • 1Academy for Engineering and Technology, Fudan University, Shanghai 200433, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|July 5, 2022
PubMed
Summary

Developing an embolic agent injection robot requires precise control of flow velocity. This study presents a novel arterial pressure-injection flow velocity model to prevent reflux during embolization procedures, enhancing safety and efficiency.

Keywords:
Embolization therapyInjection speed planningInterventional therapySmart injection system

More Related Videos

High Throughput Single-cell and Multiple-cell Micro-encapsulation
16:19

High Throughput Single-cell and Multiple-cell Micro-encapsulation

Published on: June 15, 2012

18.8K
Scalable Fluidic Injector Arrays for Viral Targeting of Intact 3-D Brain Circuits
13:36

Scalable Fluidic Injector Arrays for Viral Targeting of Intact 3-D Brain Circuits

Published on: January 21, 2010

14.5K

Related Experiment Videos

Last Updated: Sep 5, 2025

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

8.5K
High Throughput Single-cell and Multiple-cell Micro-encapsulation
16:19

High Throughput Single-cell and Multiple-cell Micro-encapsulation

Published on: June 15, 2012

18.8K
Scalable Fluidic Injector Arrays for Viral Targeting of Intact 3-D Brain Circuits
13:36

Scalable Fluidic Injector Arrays for Viral Targeting of Intact 3-D Brain Circuits

Published on: January 21, 2010

14.5K

Area of Science:

  • Medical Robotics
  • Biomedical Engineering
  • Interventional Radiology

Background:

  • Interventional embolization therapy relies on precise embolic agent delivery, often under X-ray guidance.
  • Current methods are experience-dependent, risking reflux, ectopic embolism, and complications.
  • Robotic systems promise reduced radiation exposure and improved outcomes, but require controlled injection parameters.

Purpose of the Study:

  • To establish a critical flow velocity model for embolic agent injection to prevent reflux.
  • To provide a design basis for controlling embolic agent injection in robotic systems.
  • To validate the model's efficacy in avoiding reflux and optimizing injection time.

Main Methods:

  • Fluid dynamics simulation and experimental validation were employed.
  • An arterial pressure-injection flow velocity boundary curve model was developed.
  • An in vitro experimental platform was constructed to test the injection system.

Main Results:

  • The developed critical flow speed curve model effectively prevented embolic agent reflux.
  • Injection time was significantly shortened using the model-guided flow speed.
  • Exceeding the model's flow speed limit increased the risk of embolizing normal vasculature.

Conclusions:

  • Designing embolic agent injection flow speed based on the critical reflux flow speed curve model is valid.
  • This approach enables rapid, reflux-free embolic agent injection.
  • The model provides a crucial foundation for the development of embolic agent injection robots.