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

Machines01:19

Machines

559
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
559
Machines: Problem Solving II01:30

Machines: Problem Solving II

650
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
650
Machines: Problem Solving I01:22

Machines: Problem Solving I

697
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
697
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

488
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
488
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

754
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
754

You might also read

Related Articles

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

Sort by
Same author

Cancer Cells Degrade the Nanoparticle Protein Corona for Biosynthesis.

Journal of the American Chemical Society·2026
Same author

Peer Review and AI: Your (Human) Opinion Is What Matters.

ACS nano·2026
Same author

Peptide Amphiphiles Hitchhike on Endogenous Biomolecules for Enhanced Cancer Imaging and Therapy.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Designing Nanoparticle Surfaces with DNA Barcodes for Accurate In Vivo Quantification.

JACS Au·2025
Same author

Phenotypic screens for SIRPA expression reveal RAB21 as a general regulator of macrophage surface identity.

Cell reports·2025
Same author

The Binding Affinities of Serum Proteins to Nanoparticles.

Journal of the American Chemical Society·2025

Related Experiment Video

Updated: Jan 22, 2026

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
10:40

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

1.6K

Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning.

Benjamin R Kingston1,2, Abdullah Muhammad Syed1,2, Jessica Ngai1,2,3

  • 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.

Proceedings of the National Academy of Sciences of the United States of America
|July 10, 2019
PubMed
Summary

Researchers developed a new imaging technique to study how nanoparticles reach tiny tumors (micrometastases). This method shows nanoparticles are more effective at reaching micrometastases, paving the way for targeted cancer therapies.

Keywords:
3D microscopyimage analysismachine learningmetastasisnanoparticles

More Related Videos

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Related Experiment Videos

Last Updated: Jan 22, 2026

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
10:40

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

1.6K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Area of Science:

  • Oncology
  • Nanotechnology
  • Medical Imaging

Background:

  • Metastasis significantly impacts cancer patient survival.
  • Targeting micrometastases with nanoparticles offers a potential therapeutic strategy.
  • Investigating nanoparticle delivery to deep-seated micrometastases is challenging due to their size and location.

Purpose of the Study:

  • To develop and validate an advanced imaging and image analysis workflow for studying nanoparticle-cell interactions in micrometastases.
  • To quantify nanoparticle delivery efficiency within micrometastases at single-cell resolution.
  • To explore the relationship between micrometastasis physiology and nanoparticle uptake.

Main Methods:

  • Combined tissue clearing, 3D microscopy, and machine learning-based image analysis.
  • Developed a high-throughput workflow to profile 1,301 micrometastases.
  • Utilized machine learning models to predict nanoparticle delivery based on micrometastasis physiology.

Main Results:

  • Nanoparticles accessed a higher proportion of cells in micrometastases (50%) compared to primary tumors (17%).
  • Micrometastases showed higher nanoparticle delivery due to proximity to blood vessels and shorter diffusion distances.
  • Successfully profiled physiology and nanoparticle delivery across a large cohort of micrometastases.

Conclusions:

  • The developed imaging technique enables precise measurement of nanoparticle delivery to micrometastases.
  • Micrometastases present a viable target for nanoparticle-based therapies.
  • Physiology-based predictive models for nanoparticle delivery could lead to personalized cancer treatments.