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

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons09:21

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

2.0K
This protocol describes the cell culturing of human midbrain dopaminergic neurons, followed by immunological staining and the generation of neuronal phenotypic profiles from acquired microscopic high-content images allowing the identification of phenotypic variations due to genetic or chemical...
2.0K
Reliable Identification of Living Dopaminergic Neurons in Midbrain Cultures Using RNA Sequencing and TH-promoter-driven eGFP Expression10:54

Reliable Identification of Living Dopaminergic Neurons in Midbrain Cultures Using RNA Sequencing and TH-promoter-driven eGFP Expression

10.0K
In Parkinson's Disease (PD), Substantia Nigra (SNc) dopaminergic neurons degenerate, leading to motor dysfunction. Here we report a protocol for culturing ventral midbrain neurons from a mouse expressing eGFP driven by a Tyrosine Hydroxylase (TH) promoter sequence, harvesting individual fluorescent neurons from the cultures, and measuring their transcriptome using...
10.0K
Isolation, Culture and Long-Term Maintenance of Primary Mesencephalic Dopaminergic Neurons From Embryonic Rodent Brains08:45

Isolation, Culture and Long-Term Maintenance of Primary Mesencephalic Dopaminergic Neurons From Embryonic Rodent Brains

14.5K
The causes of degeneration of midbrain dopaminergic neurons during Parkinson’s disease are not fully understood. Cellular culture systems provide an essential tool for study of the neurophysiological properties of these neurons. Here we present an optimized protocol, which can be utilized for in vitro modeling of...
14.5K
Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice09:35

Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice

9.2K
This protocol describes two different environmental manipulations and a concurrent brain infusion protocol to study environmentally-induced brain changes underlying adaptive behavior and brain repair in adult...
9.2K
Primary Culture of Mouse Dopaminergic Neurons11:58

Primary Culture of Mouse Dopaminergic Neurons

39.1K
Dopaminergic neurons play a vital regulatory role in the brain. Their loss is associated with Parkinson's disease. In this video, we show how to generate primary cultures of central dopaminergic neurons from embryonic mouse mesencephalon. Such cultures are useful to study the extreme vulnerability of these neurons to various...
39.1K
Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area09:54

Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area

26.4K
Dopamine is distinctly regulated in the midbrain nuclei, which contain the cell bodies and dendrites of the dopamine neurons. Here we describe a dissection and sample-handling approach to maximize results, and thus conclusions and insights, on dopamine regulation in the midbrain nuclei of the substantia nigra (SN) and ventral tegmental area (VTA) in...
26.4K

You might also read

Related Articles

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

Sort by
Same author

PARROT: Phase-Altering Regulatory Rewiring Over Time.

bioRxiv : the preprint server for biology·2026
Same author

LOESS and DE-SWAN can induce artifactual "waves" of molecular aging.

bioRxiv : the preprint server for biology·2026
Same author

CAR T-Cell Therapy for Cancer: Updates and Challenges for Response Assessment.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.

Research square·2026
Same author

Genomic, Transcriptomic, and Regulomic Analyses Do Not Support Profound Autism as a Distinct Biological Category.

bioRxiv : the preprint server for biology·2026
Same author

Deploying a JupyterHub Server for Academic Research Using Netbooks as an Example.

Current protocols·2026
Same journal

Escaping the trap.

Nature reviews. Cancer·2026
Same journal

A genomic and epigenomic lens into the biology of acute lymphoblastic leukaemia.

Nature reviews. Cancer·2026
Same journal

Systemic health impact of cancer-associated extracellular vesicles and particles.

Nature reviews. Cancer·2026
Same journal

Imaging the hallmarks of cancer.

Nature reviews. Cancer·2026
Same journal

CLIM-TIME links tumour genetics to spatial immune architecture.

Nature reviews. Cancer·2026
Same journal

Serving sulfur to boost anti-tumour immunity.

Nature reviews. Cancer·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice
09:35

Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice

Published on: January 20, 2015

9.2K

Artificial intelligence in radiology.

Ahmed Hosny1, Chintan Parmar1, John Quackenbush2,3

  • 1Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.

Nature Reviews. Cancer
|May 20, 2018
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) algorithms are revolutionizing medical image analysis, offering quantitative insights in radiology. These deep learning methods enhance disease detection and characterization, particularly in oncology.

More Related Videos

Primary Culture of Mouse Dopaminergic Neurons
11:58

Primary Culture of Mouse Dopaminergic Neurons

Published on: September 8, 2014

39.1K
Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area
09:54

Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area

Published on: August 10, 2012

26.4K

Related Experiment Videos

Last Updated: Jan 8, 2026

Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice
09:35

Environmental Modulations of the Number of Midbrain Dopamine Neurons in Adult Mice

Published on: January 20, 2015

9.2K
Primary Culture of Mouse Dopaminergic Neurons
11:58

Primary Culture of Mouse Dopaminergic Neurons

Published on: September 8, 2014

39.1K
Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area
09:54

Comprehensive Profiling of Dopamine Regulation in Substantia Nigra and Ventral Tegmental Area

Published on: August 10, 2012

26.4K

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Traditionally, physicians visually interpret medical images for disease detection and monitoring.
  • Artificial intelligence (AI) algorithms, especially deep learning, show significant advancements in image recognition.
  • AI offers automated pattern recognition and quantitative analysis of imaging data, complementing qualitative assessments.

Purpose of the Study:

  • To provide a general understanding of AI methods in image-based tasks.
  • To explore the impact of AI on various aspects of radiology, with a focus on oncology.
  • To discuss the advancement of AI in the medical imaging field.

Main Methods:

  • Review of AI algorithms, including convolutional neural networks and variational autoencoders.
  • Focus on AI applications in medical image analysis and pattern recognition.
  • Exploration of AI's role in quantitative assessment of radiographic characteristics.

Main Results:

  • AI methods excel at identifying complex patterns in medical images.
  • AI facilitates quantitative rather than qualitative assessments of imaging data.
  • AI demonstrates significant potential to advance radiology practices, especially in oncology.

Conclusions:

  • AI, particularly deep learning, is rapidly advancing medical image analysis.
  • AI integration promises to enhance diagnostic accuracy and efficiency in radiology.
  • Addressing challenges in clinical implementation is crucial for widespread AI adoption in healthcare.